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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Learning Scikit-learn: Machine Learning in Python"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "IPython Notebook for Chapter 3: Unsupervised Learning - Principal Component Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Principal Component Analysis (PCA) is useful for exploratory data analysis before building predictive models.\n",
      "For our learning methods, PCA will allow us to reduce a high-dimensional space into a low-dimensional one while preserving as much variance as possible. We will use the handwritten digits recognition problem to show how it can be used_"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Start by importing numpy, scikit-learn, and pyplot, the Python libraries we will be using in this chapter. Show the versions we will be using (in case you have problems running the notebooks)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import IPython\n",
      "import sklearn as sk\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "print 'IPython version:', IPython.__version__\n",
      "print 'numpy version:', np.__version__\n",
      "print 'scikit-learn version:', sk.__version__\n",
      "print 'matplotlib version:', matplotlib.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "IPython version: 2.1.0\n",
        "numpy version: 1.8.2\n",
        "scikit-learn version: 0.15.1\n",
        "matplotlib version: 1.3.1\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import the digits dataset (http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html) and show its attributes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_digits\n",
      "digits = load_digits()\n",
      "X_digits, y_digits = digits.data, digits.target\n",
      "print digits.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['images', 'data', 'target_names', 'DESCR', 'target']\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's show how the digits look like..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_row, n_col = 2, 5\n",
      "\n",
      "def print_digits(images, y, max_n=10):\n",
      "    # set up the figure size in inches\n",
      "    fig = plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
      "    i=0\n",
      "    while i < max_n and i < images.shape[0]:\n",
      "        p = fig.add_subplot(n_row, n_col, i + 1, xticks=[], yticks=[])\n",
      "        p.imshow(images[i], cmap=plt.cm.bone, interpolation='nearest')\n",
      "        # label the image with the target value\n",
      "        p.text(0, -1, str(y[i]))\n",
      "        i = i + 1\n",
      "    \n",
      "print_digits(digits.images, digits.target, max_n=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x104332210>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's define a function that will plot a scatter with the two-dimensional points that will be obtained by a PCA transformation. Our data points will also be colored according to their classes. Recall that the target class will not be used to perform the transformation; we want to investigate if the distribution after PCA reveals the distribution of the different classes, and if they are clearly separable. We will use ten different colors for each of the digits, from 0 to 9.\n",
      "Find components and plot first and second components"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_pca_scatter():\n",
      "    colors = ['black', 'blue', 'purple', 'yellow', 'white', 'red', 'lime', 'cyan', 'orange', 'gray']\n",
      "    for i in xrange(len(colors)):\n",
      "        px = X_pca[:, 0][y_digits == i]\n",
      "        py = X_pca[:, 1][y_digits == i]\n",
      "        plt.scatter(px, py, c=colors[i])\n",
      "    plt.legend(digits.target_names)\n",
      "    plt.xlabel('First Principal Component')\n",
      "    plt.ylabel('Second Principal Component')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At this point, we are ready to perform the PCA transformation. In scikit-learn, PCA is implemented as a transformer object that learns n number of components through the fit method, and can be used on new data to project it onto these components. In scikit-learn, we have various classes that implement different kinds of PCA decompositions. In our case, we will work with the PCA class from the sklearn.decomposition module. The most important parameter we can change is n_components, which allows us to specify the number of features that the obtained instances will have."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import PCA\n",
      "\n",
      "n_components = n_row * n_col # 10\n",
      "estimator = PCA(n_components=n_components)\n",
      "X_pca = estimator.fit_transform(X_digits)\n",
      "plot_pca_scatter() # Note that we only plot the first and second principal component"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3P0fX1K7IQAZmTZyFgMAAdHm6S4Hrk5GRgS5duqBHjx6Ijo6+By38b4wEMB/A\nUYhf7zZk2w/uBWYAuwA86T6/+I/7F27zDOPj4z3H+vXr/5NG/v/Khg0baLP5Ua+30m4PyNOL5XZ0\n69aXanVCjrf1uXziiSdvm/bmW/mhQ4cYGFiMRmMoxUI2I8UiuWcISDQa/dxeTRMJNCCQ4c47nkB1\nij0hfCn2qM50Tz+F8PPPP2fZstWoVuspSd4MCYmmyeRDk6k+TaZ2tNn8Wb5WLWELeOIJMe2zYIF4\n427bltDpxHqH5s2JTz8Vb+1TplD13HMizahRNDZqxJIVKtDPL4wIL0VcvSre8pcvp8ZkEqMBl4t4\n912iZUvCaqVXUBCjK1Wi3m6nyt+fqF+fGDBAvOEHBxNeXkSnTuyu09FpMmV7Ou3aRZQrJ0YNO3eK\n0UadOkSFCqLeO3aIkUZgIE2BgRw0aBChVlOl04lDq6XOZCLeeYd4+23KTid37NhBUmwE9Mxzz9Fg\nNtPk7c2Xpk4t8JTIBx8sYFCQnVarkV27tvXsCf6ogNu8hQ98biAboiETkMAEJLAf+jEyKNJzPy0t\njVEhUayvrs/hGM7mqub0d+QO9li5XGV2R3dPHq3Qih1adyhw/bKystihQwc2a9bsjpsOrV+/Ppes\nRCEarhsCeNV9NLiH+eogVnQPzXHtIMQ0EyAi0SrTTYVA9sKyb92C+CtaLL68fPlyvp4/cuQIbTZ/\narX9qNEMpcnkvMV99MCBAyxevAJVKjUDAiIZEVGOKtUbBDYQspno3Y9o3IaQShAYyCeeaEovrwDq\ndDItlkACQQRiKTYC2kPhGit2iQMqE9hEjSaKiYmJdLlcbNeuG02mkrRY2lOSnBw+fAT/97//8dSp\nU4ytV49YtUq4jT72GBETQ7XVStSsKQT+X38RYWHCS6hGDbHugCT69ydGjCBKlybKlKGqWDEh4C9f\nzrY5aDTUeHlRVzySBllHo7eZkpcXneHhwtbQt69Y3zB1qlBG1asT9eqJfM1mdgaEMjp0iPjhB2Gn\nmDiRGDZMTEuFhgrFU706Ub484eNDLF4sjPfdu1MnSfR2OBgREUFZlvnMM8+wTIUK1EoSG7drl8se\n8fy4cZQbNBC2kaQkyqVLc9FHH+X7e7Nu3ToGBcnctQs8exZs187IPn265Pv5h4HbyZfExET6yD58\nDs9xJEayjFSG/fv2z5Xm6NGjrF2tNh1WB6vEVLll+9JGcY3YHM09SqK2pjafe/a5AtXN5XKxR48e\nrFevHm+jZetvAAAgAElEQVTcuFGgNty8fg/ld6GjAvA/AK/94/p0CIM1AIyBYrguFHIvLBOH1Vq+\nQOGq//rrL06dOpWTJk2+ZV40LS2Nfn4RBN4k8BQBPcUucZcJS9Xcge069SDQgk8/nT1vvn//fvdK\n6obuEYSNgNWdV5Lb88lEaE3UGc1MSEigyVSGwHV3e3bQZPL2vCV/sHAh5chIsVZh2TLK/v4sUaWK\ncE3NzCTq1iV69BBv7q+8IgTzxYvC1bR8eWLkSFFXl4vo0IEYPFicz5nDYhUq0G7RsnlFcFI7MMQB\nGnQQ9okDB4RS0euFwDebienTRQiP6tWJwEBqJEmMRGrUIGrVIt5/nxg7VigOu10skmvdmqhdW4x6\nGjYUZe/fT8TFMTgkhBMmTGBCQgLbtWvHgIAA9u/fn0ZJuuUzK1m1qlBEN/v+3Xf5VB77JbtcLr4x\nZw7Dy5dnREwM573zDseOHcWJE7MtLkeOgGFhjnx/Zx4G8pIvc96cQ6eXk2bJzG6du93V9qVeJi9W\n01VjZUNl+nn7FWj7UpLs27cvq1WrxmvXrv1rurzagEJSEm0BHAZwBSJM+FX3//+VmhC2jl8gjOK7\nIWI2ewP4DooLbKHy119/uReWnXQL1eM0Gu33LN7/77//TrM5gsBIAk+6hXdZAosJc3Eh4OgWNdOm\nUWu088svv+S1a9e4ePFivvvuu6xUqbp7mukaxd4PLQkMdtd3LWGyEZMmEUOHUSWbKElP5lB6LqrV\nulyurR8sXMjYevVYtWFDDh82jLJRQ7UaNBlVwoicmUn88APlru0pBfkQvXuLkUWZMsT332fXd/Fi\nwmajytubOqeTDRs3ZpVI0PURyMXgkVmgUQeR7q23iCpViEuXiLQ04skniaFDsxfQGY00WK3CmD1p\nkjA4v/CCKPPcOaGUhg8nIiKES63DIZTWZ5+JEYXTydp16jAhIYEJCQkcPnw4TSYTW7ZsSaMkcfxL\nL+WaTqrdrBkxb56nLdqhQzloxIjbfobvvf8+TSVLElu2iH6JimKnLl3YqZPhZk8wMRGsWLHYPfnO\nPCgUpnz5L9uXHjt2jCqVipIk0Ww2e44lS5bckjavNqCQlEQShGvqg8RdfUAKuZk06RXKciAtlqco\ny4GcNi1/3kl34vjx45w5c6Y7fEWsmF4CCewi4E0YA4Vf/9mzxJ49hK8vhw4dxkuXLjEqqhzN5kaU\n5a7UaBwUAf5uCv7vCVQV/1vqEsuXZwvuMWOokawE9hFwUaWaxeLFK9y2fjt27KC/Q+KvU8GM/4FD\nmmtpktTE6tU02SXO7ga+0RU0G1WMLluWWi8vol07IiNDuKDWbkToLUKxeHkRBgPbVREKgovB6x+A\nGjXE2oiuXcXI4GY9f/hB2BNIsQDPYGBgeDjVo0cLhTBqlBg9vPSSZ+SimvoyrTY9Te3cNhOzWXhA\nbd5M1K9Pi8PB4cOHc8KECaxatSqtNhu1skx89hnlChX41rx5JMVc9qZNm2hyOmns1Yty+/b0i4jI\nU1g93qSJmKJjtnKs26oVy5SJ4JNPyhw0SE+nU+a33357T743DwqPgnzJqw0oJCVxL9ZE3Gvuc5c/\nuuzatYtLlizJV/TT23HhwoVcdox9+/bRavWjJHWlTtdCTAlhbA5B/wyBttSYw6gzmegVEMC33eE6\nXnppMvX6nFFem1Ol6srs6K6jqVI5CLxE2AKJjRuzBdibb7JG/QY0Gi3U6UyMjCzHXbt2ce3atdyx\nY0eut+lZs2ZxYGO9R6hfWyCEutnHwgV9soX9O73Ax6uUpxQRQVSuLN7iTSZCKkPIMqU61Wg2aykb\nQEkPrhgMJr0GdnoctJg1QhlUrSpcU2+6s06ZIkYCPXsKA3atWkLoG43UeXkRsiwM1LGxxOnTVL86\nnWFBen46FHy9KyjZTcKuodUSKSnEzz9TbTZTrdVSo9dTZzSKaammTYmsLGLSJPpERLBugwa0WyWa\nZR1Dg5wcPXo058+fz+Tk5Dw/20Zt2+YadWDWLLbp2pVXrlzh/Pnz+eqrrz7U+3rnxaMgX/JqAwpJ\nSbwBESq8E8TUU1sAbQqjoAJwn7tc4Z+kpqayYcPW1OnM1Olkduz4DDMyMli//pNUqWZ7BL1GM4Qa\njZVAHIG6FAvjztFsrnvLIqBnnx1EYGYOJbGZarXVPU1Vi0A4gQBWqlSFZi+HWI/w00/Ed99RCgri\nmjVrmJmZyUuXLnHPnj202wNps9WhyRTFZs2e8niCLFmyhLXKmJi5SCiDTeNB2WGhtVgglwzIVhIf\n9QeDAu3CjuFyCeP2tGmENoCyr41daoCHZop0ei1oDvWhyctI6bEYMaWk0dDHx0eMOGJihJHc4RDn\nsiwWu9G96E2ShAJyOKirWpV6o5EarZZGSc8dL2XXaUQzUGU1U2WxiDJSU8VoxWoVxvWWLUXeTqdY\n1W21EmPHUjJpuSVB5LG4Pxga5MP09PR//Yy3b99O2ekkJkygatw4mpxO/vLLL4X1lXpgeBTkS15t\nQCEpiQ/dxwf/OIqS+9zlCv9k0KCRNBrbut1Rr1GW63Hq1BksW7YGgfU5BP0HbNq0PRs2bEq9vhiB\nt6nX92FkZNlbjG+ffvopZTmawDECKTQa29PHJ4rAiwS+InCFwEoCdmq1rQjtOMJSnCqbnS+OH58r\nrzJlqjF77+k0mkw1+OGHH5IUbqCVK5Ri2RCwfTVQNuuEIli1ig6HgZ8NA1cNA4N9JPqHhhLPPCM8\nj4YNE4LcaKRGDaYvzBbeTSuqxBv8jRsira+vGCk0bCgUQ69eIraSv78YCRQvnnvBXalSxGOPUWcw\nMCY2lhMmTODYsWPp7+/HMS2yyxnWBIQsUa3T0SlJwiBeqpR442/aVCiHCxeEUvP3J1asIFavZtVK\nNk8eXAwG+ci3DefwT/bs2cNhI0dy+KhRt3jrPKo8CvIlrzagkBbT9ShopgpFT1ZWFk6ePAm73Q6z\n2XzP8//hhx24cWM8AD0APVJTe2PjxlVo2rQekpKm4vr1GAApkOXX0br1YHTo8BRmzpyFXbs2oUSJ\ncIwbtwkmU+51mW3atMFvvx3BpEllkZmZjgYNWuPChQicO5cMoAQAC4AjALKQmTkTQBRwdTKIl5Ce\ndiNXXn/99QeEHwQA6JGSUg9HjiQBAI4ePYp9B8/ixo3nse/c28DggUBzsUgqeVN/dFs0H2oXkQ4J\nrnLlxIK7SpWA8HDg8GEgJQWqksVx5jIQ7BBq6MQ1HfDbDkCWAb0e0GhE2oAA4O+/gfLlxQK7I0eA\nhQuBc+eAzZuBGjWA118H/vgDGDcO2r17UTU2Fmq1GgaDAbGxlTFv/beoFJGBv84Dc380Art/gWvf\nPqR0aotGpYCzWX/j93lvIHXDVqBiRZFXbKxYuLdvH6xfLsPhw1cw7zugX33g8GngSmoWnE7nHT/n\nMmXKoFf37lCpVChRosQd0x87dgy//PILgoODUbly5TumV3g0CAGwCsA59/EpgOAirdEjoOkLk0OH\nDrFEiRL09/enyWTia6+9ds/LaNu2KzWa8R5PIr3+WQ4cOILp6ens2vVZ6nQS9XoT+/UbzFWrVtFk\nctBsjqIs2/npp7fGmsnJxo0bOeaFFzh06FBKkjeB4gS8KNZG+LpdYVd7Rit6fXu+8kru+EKPP96Q\nGk2C255xnipVJF9//XWeP3+eHTt2pF5fThjUNUbxxv/qq8TcucIQbTKJ6aGcoTQWLSIqVRL/f/st\nNaWiGewNvtwerFxMTZ1RKzyhbr7dR0fnHimUKyemxh57TAT669aN8Pam0UtmiAPsUENNu01Ls9HA\nevXrMyEhgRMmTGDx4sVpNJvp6zRRtuiFoZ+kudZjfKe3GBW4PgKfrGEgXp4iyt20ybMJkd2s4hcj\nwK9Ggv42sEIY6OctccG779zxM75y5Qpja9emKTycpvBwVqlbl1evXs0z/eeff0anU2bz5laGhckc\nMqTvHct4EHkU5EtebUAhTTd9BxEBVuc+ekBEaS1K7nOXP1xUrlyZb7zxBkkRljskJISbN2++p2X8\n/fffDAiIotVajxbL4yxWrDwvXLjgub969WqaTN6U5VCKldWvuoX6Tsqyg6dPn75tvosWL6YcGEhM\nmEA82ZqQwwhcJnCBIsprXQLe1Gis1Gr7UKUKJqClXm/im2++7cln//79bnuGH0WgwKa02wMZGebP\nzjV1HNkMNBtlAm0pG1VUq8XaBlXbNsTevUKYN2qULeQTE4Vwf/ttsWfD1KlE7drUGXSUZZlxcXGM\nLlGCOqdTKAq7XXgvkcRXXxEmEw3e3tRLElUjR4rpqBYt6PDS8OoCIeyPvQ7qNKDWaKR/SAi9QkKo\ni4khrFY6Q0OEMnOvyrZEBvDXqdnTR7OeBo2xMTQ4HDRGRVGuVo12m4EfPJudZuVQ0MtHZqOWLW/p\n9+TkZH777be5jPwDhg+noVs3YQDPzKSxc2cOGTXqtp9bVlYW7XaZ27eLHrt8GYyMNN3z79394FGQ\nL3m1AYUY4C8/1+4n97nLHx6ysrKo0Why7aPQr18/zp49u0D5XL16lVu2bOGBAwdyeQZdvnzZowwu\nX77ML7/8kl999VWu9QjJyck0mRwUmwgJA7TYkvQCAdJmq5ZnCBCf8HBi27Zs4dy4DYG33fl0p1ot\nsV27p7ljxw4WK1aBGs1AihAdR2g0BnvcMbds2UKLJZZiE6PzBEiDwYfd62g9QvOrkaBN0rBbLeG2\nuv8V0NtXFvsx7N4tBP3vv4tQGjYbERkpRhm7dmWvMzAY2L9/fyYkJDA+Pp6hUVFi85/ISKFUbDZC\nr6dZp+NjlSuzdevW9A0Lo6ZDB6JXLz5WXJXLVmA3QdgsVq4U3lsZGWLVt1qVbfCuUoWSScdOj2uY\nthA89RYY6afmYzVrskOHVgwKMrJ6dZleZjVnd8vO+8O+oKVhLWoMhlxB+3bv3s0AXy/WKW9lVJCJ\nHdu1ZFZWFqs3bixsNTc/i1WrWKt589t+bmIFv445xk586inLbX34H3QeBfmSVxtwF0oiPxHdkgF0\nBaCBsGE8DeB8QQtSuD+o1WqEhoZi3bp1AIDU1FRs2bIFERH53wLkt99+Q+nSpTFo0CDUr18fvXv3\nRmZmJjp37gWnMxD+/mFo0KAVtFotmjdvjiZNmkCSJM/zSUlJ0GhCATzuvlIDYobyCICjSEs7hLCw\nsNuWnXLlCpDzXrEQiPWbyTCZNmPt2kSsWLEIjz32GE6e/BNZWRMgvppRuHGjBZ58sh02b94Mp9OJ\nzMzjEEH/HAAugK4riPTJ9GQd4Qu4mIVpHQCjHigdDPSufgPYsAE4cQIWkwmG2FggMRH4808gKUnY\nGgIDRQanTiErIwM2d9BAlUoFL4sFxkvnoJH0QEICsHIldMyAIyAAzZo3R0xMDHp06ADXihXAmjXY\n9yexdi/gcgFzvxOqUNJmAVevArVri/IOHACsNlGPFSugOX8eHTp1R4pXHCy9NYgYrkW3vmMwefx4\n7N37PQ4duoEtW1IxfZYLY5YBMxKB178GBiyXcLXnAE9Qx3fmzUWT+o+jWYOa6FvrEjaMvoL9U1Lw\n5/7vsXTpUsSULAnDypWici4XDKtWoULJkrf93Gw2G/z9ffDhhze/Q8DGjZmIiYnJ79cuT2bNmg5f\nXyusViP69u2G9PT0/5ynwr0lHGI/iZs2ic8BhBZlhfAIaPrCZMOGDfTx8WGjRo0YGRnJnj17FiiI\n2+OPP865c+eSFBudVK5cmZ06PU1Zrkux+jmNRuNT7N9/+C3PulwuHjlyhEajndl7XCcRkGixVKck\nOfnmm3PzLLt9jx40tm0r3ELXrCHMZppMUTQavTly5Iu50oaGliaQ6C7jY0IyEaXLEiYTm7RqxV69\n+tNkKk29fjAlKZKy7KCXrOIPE8Cjr4ONKhroZdbw61HZc/t1y2vEpkEOBw2BgWzeogW1N0NwkCJ0\nR/36YsV406bUFS/OUhUrcvDgwezYsSNNko4L+oBlI7TUFROxm/y9wPKlIz2rol944QWqdDoxyrDb\nKRnVVKnAciHggeng7G6gWVYLu0WdOiKm1MyZuRbklahShSR5/fp1z45/7733Hrt3l2+mossFqtWg\nJGkpl4oknn+ecng4SxuNbP1kS5YNl/n5cPDtZ0CHWYykuBh8oZWKL774Io8ePcqYxx+nuXhxmqKi\nWLFmzVwB6/7J3r17GRHhRx8fIy0WAxcu/CDf37m8WLFiBaOjZf7+u4gV1bixxNGjh/znfP+NR0G+\n5NUGPGSxm/4L97nLHz5OnjzJxMREbt++vcBRPh2O3DaDcePGMSgogrlXP69juXI1cz23YcMG2u0B\n1GqNNJt9aDA4aLM1oCT5cNKkqVy7di3/+OOPfy07JSWFXfr0oT04mOaAQOp0Mk0mJ8eNm3BL2nXr\n1lGnsxFoRRhl4pdfhHhMSiIsFlavV49Tp05lp06dGBNTjVrtGAKv0CzpKRtAo6QlevakbDWwU00N\nqxRTUXaYReC9rVuJ996jUdJSssvE55+LvIcPF4vfNBoh5GfOpLZLF+psNvp46bl+nBC0h2aCNpOW\n0OvZr4GKvl5aNqhfj926dWN4RDi11atTMoDNY9X0dWg5tUP2tNBvM8BwI6jTaESojkGDRKgO0hN5\ntlJc3C39sWPHDlqteg4cCH79NTh7NliqVCidZjP7ajTsJElcBPB5tZpBvlbunJxd5qjm4LhW4Ll5\nYFSAjlqNlia9iRXKVODatWv5yy+/5BlxNCeZmZk8efJkgeMa5cWzz3blW29lT2Ft3w5WqhR1T/LO\niwdZvnTp0oX+/v60WCyMiIjg5MmTb5surzagkJREFMRI4jyyRxKRhVFQASjMz+H/PXXq1OGsWbNI\nit24YmJiqNFIBHoye/XzODZq1MbzTHJysjuq7Br3/U9ptfpy5cqVPHLkSIHrMGLEC5SkBgROUWxq\nFM1ly5bfku61116jVhtEBEbk9iaqWZNqq5UOo4NhhjDqoCHQmJCCibnzRUylxo1F2oMHxRoGjYYY\nPVpc272bkiyE95zuoFWCiJUkSSIo4LVrwnjt7S0CASYksFONbKH761TQYTMS3boxNFTmT5PAJ6vo\nGOFvoDHQl8YgJz8eKNL+rx9YMhC88A6YtQgc9ATYQQ9+BlAly2K1tyyLdRqTJlFvt3tsLz/++CNH\njx7NKVOmsG7duqxduzbHjh3LgAB/Wq1a2u1GNomLYyujkUcArgHoI0ksFubL7TkW6Q1vAlpNWpok\nLb2MMkdgBOMRz1raWmwY1zBXn7tcLi5atIgjho3g/PnzPSOZwmDs2Oc5YEC2reODD8AGDaoVWnnk\n3SuJm9uXjh9fONuXkiKiwU0FfPDgQfr5+d12n+u82oBCUhLbIWwSN72bnnZfK0rueec/LKSkpOQy\nSv+TLVu2cMSIERw/fjz/+uuvuyrj8OHDLFasGKOjo+nl5cWGDRtSkkIptgStTqAuVSovLl261PPM\n5s2babNVzTHSIC2WUrl25vonp06d4q+//nrb/aWLF69MYGuO/N5m5869PfczMjI4f/58DhkxgrGx\n1QmDUewTQQrvJIeDsNtplpwsDg0nAYwAiDIVRJpPPhHhuukWP8nJQknIMjFzJo2li+V6u/9kCGiz\n66kpXozWuCo0N60nYid5eVGt0VCtVlOj0TDEqeOb3cEwPwMjQnwolwyn1m6hZFTTzylCeOh1YJhT\nGKm/GCGmuaoUA/U6FU0GsJQBPAfwL4Baq5WIjxfeVePGES1asGq9eiTF4kMvLy/GxcWxUqVKlCSJ\nS5YsYefOndm5c2fq9Rpu2AD6+FjYt1s3hjocLB8RwcTERL41ZzaLB8lcMgCc3klFH28z161bx5Ej\nR7K2qrYnnPVwDKfdYs/12fTt2ZdhchifwBMsLhdn80bNCzxa3bt3L2tUrsFg32C2bt6a586du226\ns2fPMjLSn+3aSXz2WSOdThN/+umnApVVUPKSL3favnTw4GcZE2PihAlgtWomPv10m0LdvvTgwYMM\nCgq6beTmvNqAQlISt9uqVPFuus9cvXpVRPY0GmkwGDhmzJhbvoCrV6+mr68vJ0+ezCFDhjAgICBX\nKOIrV65w9erVnDlzJj/66KPbCuebrFixgl5eXmzVqhXLlStHs9lGYJt7pPA+jUZvJiUledInJSVR\nknwInOPNqLIGg41nzpy5bf7x8VNoMHjRYilFb++gXLGjrl27xrJlq1PsVS2UhFY7mMOHjyYp3tga\nt2lDOS6OmDqVpsqVWS0uTrx1R0SIUBS1ahElShBvv03JbOb3AL+HezRw44YYCZQqRXTvTixYQG1M\nDFXlywvPpW7daLFo+Wb3bCWxZrQQ6mE+Kn45AnyvDyh7SYRWS7PF4gmwV6VKFcqykSaDsC281QM0\nmzTEsmXE4sU06rPn/re/BHqbwd0vgw4LiGLFWL1uXdokiVaDinoNKBnU1DZr7In9pJ44kV16C2UZ\nHR3Nbt26eWwdYWFhtFqtbNasGevVq0edTsutW0GVRsWoSpUYVbEi58ydy59++omhAaFUq1R0WvVs\n3rgu9+3bR5JcsGABo+Vojsd4JiCBbdGW5aLLeT6bkydP5tqm80W8SD+TX4FCzJ87d44+Xj5soWrB\ngRjI6rrqrFKxSp4C9cKFC5w/fz5nz56d6zt3O9LS0vjBBx/wlVde4datW/Ndp5zcTr4kJyczOjqY\nbdtK7N9fBDb85/aldruBly6J147UVDA0VL5l+9JRo4bQ19fCoCD7Xe8d/txzz1GWZWo0Go/tMD9t\nuHm9MATyKwDGQhiwwyH2epgGEdLbuzAKzAd31bkPK6tWrWLZsmXZvn17pqen89y5c6xQoQIXLlyY\nK121atX4xRdfeM6HDx/OsWPHkiS///572mw2mkwmdujQwfP2efXqVd64cYOHDh3ixYsXPc/6+vp6\nfmRpaWksV64c9XoLbbbalCQnX331jVvq2a/fEGq1AdRoWlOv9+PUqa/etj2bN2+mLIe5p5JIYDGD\ng0uQFBsVOZ2hNJlKEpCpVvekJHWgn1+4x07y888/0xQZKUJvk8TlyzTY7dy2bRubtG5NU3CwMC5f\nuiTuL1jABmYzSxmNRECACJ43fjy1xYszplo1Nn3qKapNJqJTJ2EgPnmS2ser0GEWQfvWjAYjfUHZ\nAE/8Iy4G41uDKrWKtWvX9gjqYcOGUWcw8O1nstMt7AdamsQRnTuzdKQ+l8trkB3UGrRUT3uZiIqi\nj11idKCWNUsI19Y/XhNlq+PiqO7Rg5K3Nw8fPkySDAgI4KBBgzxl2+12Pv30057zWrVqsXRpNVUB\n/sT69cSmTTRERdGgl9gO7RiPeHZCJ3pbvD1uzenp6axfpz5DzCEsZy1Hb4s3t2/f7vnsDh8+TB+T\nD+MR7xltRFmjuHHjRpLCFTYpKckzBXXp0iVu374916j2iy++YBlrGc/zEzCBJoOJZ8+evcMv4d9J\nS0tjnTqV+cQTJg4bpmVAgHxXxvPbyZeJE+PZq1f2tNeyZWDNmuU99w8cOMCoKLPnPglWrZp7H+wp\nUxJYo4bMo0fB/fvBEiVkLl68qMD1I8WL0vr16+lwOHJ9Pv/WhpvXCyps8+MC2wFi29L17qOf+9ou\nADsLWqBCwViwYAGGDRuGlJQUjB49GjqdDk6nE7169cKWLVtypU1NTYW/v7/n3N/fHykpKbhx4wY6\nduyI4OBgLFy4EB9//DHWrVuHiIgIxMfHo3jx4mjUqBFCQ0MxZ84cZGZmIjk5GY899hgAQK/Xo0qV\nKpg48QV8+ukEbN36HUqVKoa1a9ciLS0NAHDy5El8/PFyZGU1QVaWL9RqGaQLt2Pfvn0gn0D2BoQd\nceLEYWRkZKBjxz5ITh6DlJTfAGyHVrsWnTqZsH//Tvj5ie3OFy1ajFSjJMJfAIDFgnStFpPHjcPC\n+fMR6nTAeuYozE+3A3bsAPR6HCXxm04nwlbcuAG89RZcR49i2/r10KtUMGRdx3PnP8bTq8dCKhuN\nzF8P4OoN4PklQL8PVDieogeCApGe7UGLtCwAajWOHjsGl0u09e+//4bGJEPSZaeT9IDq6hVAp8PR\nMy4cPi2u7z4GJF8DsjJcUI0bD93xY5A01+FrycRLbQF/L+GmO6E14LV9C4p9uAU+qWZMHD8RgAhj\nsnbtWpw/fx5JSUm4fv06dLrsgg0GA44cNYAzXgXi4oBatZA2YwYyjRLKoixUUKEESsBL5YUDBw4A\nADZu3Iizp88iQ5eB4KrB+Hnvz6hSpYonz/DwcDj9ndio2YgLuIDtqu1I1aeiYsWKmDplKoL8glC1\nfFVEhUZhyZIliAyJRIcGHVAmugymvDQFAGAymXDVdRUuiD67gRvIcmXlcqO+Gz777DO4XAfx7bcp\nmDUrE998k4rhwwf9pzxvcuHCWZQqleE5L1kSSE5O9pxHRUVBp7Nj2jQ1TpwA5s1T4eRJbS4X4K+/\n/hSTJqUiPFxsWz56dCrWrFl5V/VRqVSIi4vDU089haVLl951ux5l7kr7PiycPHmSb731FufMmcOo\nqChu3bqVjRs35pw5c0iKt4guXbrwpZdeyvXcxIkTWb16de7evZvffPMNAwICuH79ev7xxx8MCQlh\nWFhYLiPy5MmT6XA4+JF7+8pjx44xMDCQu3btYrVq1Th58mS6XC7u27ePfn5+/OWXX3j48GFGRESw\nbt26rFKlCitXrszLly9z1qxZNBh65bAhHKCXV+Bt27d8+XKqVIGexXXAZ/TziyBJms3OHCMMUqUa\nxwkT4j3PfvbZZ5SkMEIKIma+Rhw5QtXo0QyRZfZXq+lrNbNEkI5fjgDn9wIli4GwWKmSzWIVN93v\nee69pns++ywDA7z4rjvExYk5YOkg0GzVUy5bXHgwvfEGcewYVe+9R18vFRf2A1/pKMKDN23WjHpJ\norevLyMiIqjTamkIcNJm1XDFYBEo0M8MqnVasc2pUU/JrOP/sffd8VFU6/vPzE7Zna1JNptsOglJ\ngKWdU4AAACAASURBVEA6EJJAAoI06b2FJigJRZReQlBRBEGaCIrSBC5NQbEhCGLj2hBp0qQrINJb\nSLLP748JG6OgcK94vff3ffjsh5zZc87MmZ1533POW56YimaaVIEGg8BMG+hrBNu1A6tHgi1TdWP5\nzdXGI43AWlIyC1DAURhFm9HGw4cP89q1a6xfvz4dDgfDwsIoywYGBzjYrVs3tmnThppRpkE1ElOn\nlo177lwKFhsfw2MsQAGHYRjtRjsPHDjA7du3026ysyM6Mg95rGKswtSEVLZt3pbjx433GkxPnDjB\nRvUa0e3nZu0atbl3715u2bKF/po/H8WjLEABmwpNaRJN7IIuLEABH8Nj9NP8+MUXX7CoqIi102qz\niqkK66M+Q7QQDnlkiPfZHj16KO12E61WlYMGPXxHXlUk+cILL7B3b9PNkfL6dVCSxDtufxO3ki/r\n1q1jRITGHTt0V9xmzUy/STty6NAh1q+fxoAAGzMzE36TELFZs2zOnVu20hg50sABA/re1bX9Gr17\n9+bo0aPvaAw3j98LgSwBaAFgIIBHATxW+v9/Ev/Wjf074+DBgwwKCmJOTg67d+9OTdP41ltvcdeu\nXXS73WzUqBFr1KjB5OTk3/isFxcXs6CggJUrV2ZycjJXrVpFUk/r7ePjw2bNmrFr1668evUq9+3b\nVypY5HJ7wV27duX8+fN55MgRpqamUlVV2mw2Lly4kLm5g2mxWJmfr7ujejwedu/enaNHj+akSZMo\ny7m/UBLf02YL8Pb7008/8YcffqDH42GDBq0IZFHPw1STgJWPPDKYJJmSkkVRnFraxwWazQlcuXIl\nSd1QW7VKHIFWehS3pRJFs4V1zGaeAOgBaDehXGrtkc1BRVAIu1unE2XpK7psGZGdTVSpQrtF5KbR\nYPFiMCEMHNEM3D8FnNJNoMkk6gRAJ0/quZ00jYlGsJ4MKgYDFUWhoig0Gw2c3UOPv3ggCYwPBd0O\n0GEGNYPIpnXrsnPLloyTZQo2mx7NrWl0uF0McouUJdCs6p/c+qC/FeyWCbZIBs2K4BW+4zCOgZZA\nfvPNN4yvUpGhvmD1CiLNikCXr4W9swVWj1ZZu4rKmCCZsgTKNhMxdozOm61prIZqtMPOOMTRITs4\nYugIkuRTTz3FDCmjnNFahsyWaMmqpqqsV7vebT12ZsyYwVrGWt62YzCGAoRy21LJlmQuXqxvr1y/\nfp3Tp0/n4IGD+Y9//MP7DM6ePZMpKRqPHQNPngSTk8HMjLQ7EvT6VqWJH3wAnj0L9u8vs2HDzD9s\n92vcTr7Mnj2TbreDdruJvXt3/pfoS51OM/PyZPbsqTI42Peu6EtPnz7NZcuW8fLlyywuLua7775L\nm83Gzz///I7HgHukJN4B8BqA8QDG/eLzn8Rd/Tj/TejZsyefeOIJb3nChAkMDg7mpk2bOGvWLFqt\nVk6bNu2uH9C1a9fS19eXQUFBNBgMNJvNnDFjBgMDA7lx40aS+n5yZGQkt2zZQlLnwe7V62Hm5j7C\nUaPGUtNq0m6vXm6fdf78+ezatSsPHjxY6gI7i8B71LQ0PvbYSBYVFbFbt2602Wz08/NjgwYN6HC4\nCTgJiNSZ60axVy+dVP7AgQMMCqpIq7USjUY/9ukzgB6Ph08+Po6Vwsyc2BFsFG+gRZVYEyYmAnQB\nnAzwUKmS+GRcmZIY0gS0KT4UNLNODbp7N3Hhgm7YnjqV2L2bko+NCRECN44CQ3zLaEi5BKwSAgLQ\nmevcbkJV6QswFGCgy8URI0YwPz+fKckJ7FpbT/mxeQyYEaOn+gjyM7JJq1aMSEhgWoMGOj/DTX7v\nCxcoO52UJAMD/Cx02iQObAg6jAbaFYUWWaGi2mg0aqwv1Gd/9GdVsSpdisowf3+mVSxLV/5CT9Bm\nFGhWRWbEGhjlNrJCqJOPPw7u3AkOfExi3YYSzSawD/qwNmpTgUJN0bh161Z6PB727NmTgYZAxiGO\nFlioQqUAgTGI4RAMoV22s1P7Tgz0DWSIK4SzZszyPgfr1q1jiDmEozCKBShgJ3SiIijsjM4ciqHM\nQAZVg8qCgoLfCPxz586xfav2DHIGMdht5T/+UTbbfucd0OWj8qknn7qj5/ytt95iVFQgLRaVzZrV\nva3X1O/hXsqXf4e+9KeffmJWVhYdDgftdjurV6/OtWvX3rLu7caAv9C76T+Nu/5x/lvQvHlzrl69\n2lteu3Yt4+PjmZ6ezoYNG95RwrRFixYxOTmZ8fHxnDp1qneWdvLkSX744Yf8/vvvvcc2bNhAp9PJ\n7OxsBgUFcVhpAreNGzeWZmAdRSCfBoMvgYU0GgewVavOvHHjBi9dusQ6depw2rRpJHXugfvvb83k\n5HqcMGESS0pKOGXKFNarV49XrlxhUVERu3TpQqPRRuBL6lwUQyiKbs6a9bz3+q9fv85vv/3WO8sq\nKiqiUZX4w6yyyOiUUHAmQD9JYkJcHCvHxtIky6yRmMCoII2L+4FPdwBNikCMHK0TBk2YoOdeMpt1\njojiYj1HU0QElewMqjJoNYIWI5hSAfzqSTDMT/dCeqo9qClgX4CPGMAMSWKTJk28RuK+ffsyNlgl\nl4BPtAU7pOnXWtFtoCE7W0/MN3s2IQjEjRu6CNy5k6rJxEGDBrGgoIAdO3ZkgEOiAFCUZMrZtYmx\nY6nGxrJixVj6+/jTIYpcA7CzAD7ZrkyZHZoGWhSBUYiiCJEAGBrs4IIFZQL37bdBh12gEUYGI5gP\n42FmI5vDhw1n/ez69BV8WRmVqUFjOMLZB33oC19WQAVWQRVq0OiCi/3Rn33RlwFaAJcvX+79jSqG\nVqQGjUEIogyZwVIwDTBQhkx/+NMII/1kP7ZuVt419L469zFVSeUADGCMMYwjRpRd8zMTwRgtmHVq\n1in3jBcVFXHqs1PZtUNXTnhyQrlJ09WrV9m9eztaLCoDA+2cM+d53g3+F+TL7caAf0FJCHdQ51no\nWV/fu9vO7wCvAGgK4DSAaqXHfKEz4YUDOAygPYDzv2pXOt7/PcycOROLFy/G6tWrIQgC2rZtiw4d\nOmDw4MG3bXP9+nU8++yz2L17NwRBwEcffYSFCxfCZDKhT58+yM3NRb9+/bz1i4qK8Nxzz2Hbtm2I\njIxEr169cOjQIbjdbsTFxaGwsBCRkTG4cOEiBEGCx1MdV69egijGweN5FprWAeQWeDzF6Nq1C+bO\nnQuDwXDLa+vSpQsaNmyInJwcAMBHH32E5s1zcP78odIahQDMuHr1kvf64+PjIUllVCfXrl2Dj8OK\ny/NKIJWeptUzwPE9Mpz16iGtVi0AwPvvv4/o6GhcvnwJG95ZA7nIiPMBgSg6eqDsgmJjdavhd98B\no0frHA+9ekF56SVIRw/gmU5A1wxg9RfAo68CDhPwzdOAzQQ4+gDxYUCr6sCUtwRYXRXRqVMniKKI\njz/+GNs+34LYgBvYdxJYNRD49ADw5Brg6okzgJ+ffn5XAPDUBODBB4EXX0TkpEnI6dbNe3mTJj4J\nT0kx2qeLqBrkwaQPNPz84CMIWLkKrTIyED1/PgYDWAlgvAvY8gTgYwaGLRUwdyNgKLSjB3rADDMW\ni4tQ4v4BK1aU4OxZIC/PgKuXbGhyrhkiS+Nh35bfxrWYa9i9azeMMKIIRYhGNI7iKGIRi2AEYxd2\n4SiOQoGCNmiDCtDzgH2Nr6G11rBs9TK89NJLmDhoIupeq4sbuIEDOIAzOIPjOI4O6IAIROASLuFF\nvAjZKOO9j95Damoqrl+/DpvFhhElI2CAAedxHi+apqPh/QJUScS7bxtQ5XoKgpoHYeWalQAAkmjX\nsh12bNiBmKsxOGw6DGeSExu2bIDBYEBubk+cOvUPvPjidfz4I/DAAxrmzFmNRo0a4U4gCAL+2+XL\n7cYgCAJwZ3LfizshHfoUOp+ECOCmeZ8AbHdzottgPoCZABb94tgI6EppEnR32xGln/8vkJeXh5Mn\nTyI+Ph4A8NBDD2HQoEG3re/xeNC6dWsoioI2bdpg+fLl8PX1RVZWFkRRxOTJkzFp0qRySqJ79+44\nc+YMcnJysH79enTp0gVbtmyBUuotNGnSJFSpEot1696EKIpo27Yb3n33BgyG12AwHAJpgqJY8MEH\nbyMpKQmFhYUgWU6w30RkZCTef/99dOvWDYIg4L333kNJSTGAEuiJ+bbDZnOievVsHDlyEYAH0dEu\nfPjh27BarQAAk8mEunXS0Xf+VgxvUoRP9wMb9wAVRRGuUo8nAHC5XFi+dCnk4hJc9AyEKs5B0eVz\nwLVrgMmkJ807exZ49lldWeTlAcXFkAsKUKGoCFdsQP/79b56ZwOT3gSuFAKVhgK9swCLEdg8BpAl\nICeTCHvkMF6ZORMWoxGHfvoJJhbj7Dn9pWo+RWe9uFps0EmLAN3M4nZDHjgEJfkF8Jz9GScNIi5f\nvgyLxYIjR47gRhFRIwJY2Ff3/Klf9SpqTZ6Onz0G/HT2LPwEASDRFsA/zgJBeYAqAyUewqcwBJVQ\nCQ44AADNPS2w9PKr6NBWwPmT51BNlbHHcwWrpZUIKg7BJeESLhkuoWRXCfqjP8wwYyd24hN8gn7o\nh5mYCQMMOI7jkCDBBRfO4ZxXSVwQLyDYGQwA2PHNDkRdi0IoQgEAdtixGIsBABGIAABYYUUwgnFR\nvIgLFy4AAGRZhiiKuFxyGfbSf74IxnvvnoWfxw+hBgf22fZhweQF3t/56NGj2LB+A/pf7w8ZMpKu\nJWHet/PwzTffICUlBe+//w7eeOM6/Px03dyv31Vs2PDOHSuJ/0N53ImSmAogDcBOALf2afzX8RFQ\n+gSVoTmArNK/FwLYjP+PlIQoipgwYQImTJhwR/X37t2LXbt24eDBg5AkCZ06dUJ4eDj27NmDQ4cO\nYcqUKTh+/Dj27duHmJgYnDp1Cu+88w5++OEHmEwmdOnSBUlJSdi6dSvq1KkDAPjqq6/w0EN9oaoq\nACA3tyc++qgr5s6dDUBfiZw+nY2hQ4fi8OHDOHLkCAwGAwYNGoSJEyfenK0AAIYNG4b69esjJSUF\nmqbh1KlTqFQpCnv21EZJSVUIwlqkpibj449DcePGXADE7t09MGbME5g+fRIA4OLFizh1zYOPvxWx\ncitgFQTUKiI2i0U4vXEjXC4XiouL8emHH2LsjRvQIGAUnoPFE4gz1y/DUzMNaN1Kz6Lati3w44+A\nJMFdUoLiq1chA7gK4NwV/eNjBi5fB05dBDQFCPEFZq4HzJoIWdJfAX8bYFGKEH/uHNIBPA0gBUDu\nGX0GlQ/gvBHwUUtwLaEarucXQPn0U3gOHECHa61w5toZbBDXI8xfwktzZsDP14EfTp1FIEqQ+ouE\nvUs/ASzFV1CzIvD++2uxTtcRcAL4yKDilVdeQWJiIr74/Avk9snFieITIAgBAn7Ejwhxh+HY4X3Y\nRqDi9evYByARwA3hBpKYhN0lu3EBF7Aaq3ESJyFAQAlKIEGCBg3bsR0OOBCGMDjhxNt4G0dxFACw\nw7MDJ9afgMvmgtFkRIlaghqFNSBDxk7shD/88QN+wH7sRzSicQ7ncARHYJSMSE5OBqCvCh579DG8\nPONlVL1WFaeMpxAQHYBP132Kt99+GyTRokULuN1u7z0pLCyEbJAhlYovAwxQBMWbHdbPzw979pxC\n5cp6/d27FVSqVDaZ+D/8+dgCfcp3rxABYMcvyud+8bfwq/JN/EU7e38Nrl27xmXLlnHu3LneQKk7\nxfbt2xkdHe3d4/V4PAwJCWFWVhbdbjfnzJnDcePG0eVy8eDBgzx+/DidTme5fDtpaWncuHEjV6xY\nQZfLRVVVmZOTQ4/HQ4/Hw/79+7NBgwbe+s8//zwrVarEN954g6+88gqdTic3bNjAlJQUzps3z3sd\n586do8fjYWFhITdt2sT333+fly5dYlFREZcvX86GDZvQZgugweBPncP6pmfUaqalNeS6dev4xhtv\nsEJ4AM02heb6mTq/Q1YWk0WREZLEiNBQKopCyWDgWFGkp7STRMjsgR4cgiGUIROKUY/ADgnxehYJ\nFgutADMADoBuj6gSDD7WBKwaotsgQn11G0PHWrrn0cAG4OJ+ugeSyw4KAP1UMN4EVpJAA0ARoEkG\nH20CvtIXrOQP1lAMHCGK9IfEaIQyGhIrKLrBed8UcNNo8L3hoMME+il6GpA3H9MN8adf0O0OR6aD\nqgQGwJcVEEiLYimXGmXVqlX0s/oxQopgsimZPhYfzps3jyk2G39xc1kBYG/09rrUKlCYilTmI58D\nMZAaNGYikyaYmItcBslBtBgtVKBQgkQNGhUolCEzDGHsi75sh3ZURZV2o51B5iCaDCYKEOhr86XN\nZKOPwYcSJIYGhHL79u0kyXfffZdOp5W+vir9/Mysm12XvXv3/sN0MsXFxUyKS2ItpRYfxIPMMmTR\n5edk27YNOWBAX65evZpOp8bcXJUtW2qsXDm8XKDoH+F/Qb7cbgy4RzaJhQAqQPdyupnIndBXGH8G\nIqAnELxpkzgHwOcX35/FbyO7OW5cmYNVdnY2srOz/6TL+Wtx9epV1KtXD5qmISIiAm+++SZWrVqF\nrKysP24MoLi4GOnp6ahRowbat2+PVatWYfPmzTh//jwWLVrkvS/Dhg2Doih44okncP/998PtdqNX\nr15Yv349XnvtNSxYsAAtWrTAW2+9hbCwMKSmpkLTNNhsNly7dg0ffPAB/P39AQApKSmYNm0aateu\nDQCYMGECzpw5g7i4OHzyySfo0aMHOnbsiMuXL8Nms2HlypVIT08vd91TpkxHfv5iXL26FMBTAK4D\nWAKAEMU2MHjWQ4IIUbmGsa2JdjWBxZ8ZMOnrYFwdPBzakMEItBbDbDHDoAXi4J7vcaykBHboD2kU\nZDRBTwQhCEuNS1G5YWWs3bwZyM0FJkwAzp+HWLMmwo4cwYEbNyAAqKoAiSlAdCCw6wTw3nbg0wKg\nWmli/PpPAVu+A2LdwOEzQGY0sGUvMKYFUCsGeGoVsOcAcFoAzCqQWgHYdgR4uB6wZD0QfF3AbqgI\nYyE+BzEHwLIoYOMYwCgDefOBtR8Bp28AVpMATwkR5gK+fabsvoXmiWh+/mG44MJhHMZHIR/hwLED\nKCkpwf79++HxeLB9+3acPHkSTZo0gZ+fHyoGBWN90Q3UALAVQD0IGIhhMMEEgpiMyeiMzggpZSV+\nG29jG7YhDWm4pF6CGC2i50M9MWrAKFRRInHFeBY/Xj+Pyzdu4GE8DJ/S1/U9vIfqA6sjJycHcXFx\nEEURiqLg6NGjyMt7GFs2f4BLl28gukI0DMZr+O67YwgKApYuBa5cAdq0Bq5fF6ApGj746AOkpqZC\nEIRyK9Ob2LZtG3rm9MSPP/wIm0OF3ecMBg++hm3bJLz2mh+WLVuLzz77DGazGR06dIDNdue74/9L\nNonNmzdj8+bN3uPjx48H7tImcScoKP3cdH29+fefhQiUX0l8h7JQXHdp+df4S7XyvcSMGTPYokUL\n70pgzZo1TExMvKs+zpw5wwcffJDp6ens3bs3z5w5w7i4uHKJ0MaPH8+hQ4eS1HM4DRgwgBkZGczJ\nyfEG7/XtWxbYc/HiRYqiyI0bN/4mx1P16tW5YcMGbzk/P5+PPvoo+/Tpw8GDB9PlcvG9994jSb75\n5psMCAj4TUxH9er1WcZTfZFANYpiAFXVTRFmRqMae6EXK7vU8mkswqxUw8PYJUOg51Xd0+mh+gqT\n4qJZVZL4JMDqAMPh5DiMYy5yaVEs3L9/P01OJ3H4cFmsxOOPM0JRvDPsQ6WrCUUCLapOJXr2xbJz\n98rSVwdcAh6Yqs/qsyqD517UvZmsRrB2rL7iODJdr/fdZD2DrEMTOfHpp9mkSRMOEkUSYBHAYAU0\nl3pVmRWwlgxaRJGfffYZZ86cSU3VXXNv0o9aZYPXzXQABjDQL5Bnz55lUlwSbQYbZchUFZWqolKR\nFT7U9yHaFCstkBgMiUaAEiRmI5sP42FmypnUJI2d0IkFKOBYjGWQEMT4+Hg2adCE99W7jzNmzGBm\nRiadmok9uhq4di3YorlAqyZ6VyQFKGA1oRqnTJlS7ne+cOECo6Lc7NoVnDoVDAuUaTEaOGsWWFSk\ne1z5++sxEZHBCnuiJ/3hTx+7kYpioNmscPz4Md73o6SkhM2a3U+zGXzgAfChh0BZ1tvf9Ihq2dLM\nBQsW3NU79Ev8L8iX240B98gF9iaspZ8/GxEoryRuGqwB3RYx8RZt/uJbfu8wZswYjhs3zls+cuQI\n3W73v93vxIkTWa1aNS5YsICvvPIK/f39+eWXX962/sqVK1mrVi2vD/vnn39Ol8tFUt86ev311zll\nyhRu3LiRixYtYnh4OBcsWMBJkybRYrEwLS2NVapU4bvvvsvU1NRyfVerVo1ff/11uWNpaXUJPEDg\nJQLXKAjj2bRpGw4YMIAKKrEN2jAXuQwwy7w2XxeSl18GrSaBNrPK1weXCe83HwN9bBIFi4Wy2cx4\ngEaARmiURZkL5usCI7F2bQpz5uiipLCQSEsjZJlPAjwGcAAEBjscTAT4NcDqCtg8WQ+sWzNYF/67\nJ5Wdt0KAgTWiBCZHgE4ruHoQ+GA2GOgj8o3HyuqF+IKybOLrr7/OdevWMVzTeKQ0+C8JoEMDP84H\nj80AH4gHK8tgXmkSv3B3OK2yTFUCNRmUIbMzOjMXuYzVYtn/4f7s3rk7U8VUdkIn2kw29ujRg/36\n9aPb5aZiUJiFLNZHfVphZRayGIEIqlCpQt8eGj58ODVFYzSi6Q9/BiGIPvChKqjMlDIZKAcSAMPD\ndCIjUhfwPj6gGWbej/uZghQ6zI7f5F+aN28eH3hA5U0BvmcPaDKVCXQSrF8fXLAANBkFDsIgRqiB\nrF8fvHgRPHYMrFpV4+LFi0iSo0aNoKqC3bvrbUtKQEUBz50r669jR40vv/zy3b4yXvwvyJfbjQH3\nSElUA7ANwNHSz1cAqv5JfS8D8AP0HYJjAHpC31raAGAfgPVAqatGefzFt/ze4f3332d4eDj37t3L\na9eusVevXuzcufO/3e+LL75Is9lMt9tNq9XK2bNn/279oqIiNmrUiGlpaezTpw9dLhdXrlxJj8fD\nXr16MTExkQMHDmRkZCQnTJjA1157jR07dmSnTp04YcIErlixgpcvX+bhw4fp5+fnDRQ6fvw4HQ4H\nf/jhB++5FixaRGNAAPHII0RmXcIcRqvVxf3793PJkiVUpWDGIIHDMZzBqo1+FrByEFgpWGJYcCAr\nx1Zi65oKixaBRYvAFimgUjuN+O47iiNH0qBpTDQY2LdvX86ZO5dhcXEMiIpiz4ceosnPT+dniI4m\nWrUiXn2VgsWXJphpFZx0qCrTTOB9JrAzQFNoII3+dmqRwZSkskC9HRNBowz6OcyMD9VXH1GBMpMS\n4tigQQM6fSx8rpvAD0bp9ZIA1gDooygcNmQITbJMq6TP7Ee3LFMo3z+nryoCfDW2b9WYTRo2Yqwx\nlnnIYxu0oUWyMCwgjBHuCD7S/xEWFhYysXIie6InU6QUNm7c2Bu70atXLxpVIyMRSQUKB2CAN2o7\nAhFsjdZMQhIVKHQZXAxHOLugC/ORz/7oTzPMTEEKTTCxMiozLFQopyT8/ES6fF10aA7WyazD48eP\n84nxTzA+Np51atXht99+yxkzZrB3b4U3BfjZs/rM/+hRvXz5MhgQANo0AwNFfwZqVvr4gK+/Ds6d\nCy5cCE6bBvbu3YkkWaVKCOvWBZ95pkwptGsHZmSA778PPvusSLfbcdeBar/E/4J8ud0YcI+UxGcA\n6v6inA3dLfY/ib/4lt9bzJ49m3a7nbIss3nz5jx//vy/1d/u3bvpcrm8RvA1a9YwODj4D0lQioqK\nuHLlSs6aNYtvv/029+/fzy+//JLh4eG8cuUKSZ0Dwmq1erOG3godOnSgr68fGzduTh8fX1aqVK1c\nlK3N5dKD2EjC46FYqxYrVqzIuLg4Pvzww2zatB1V0UyrYmCLZPCbp8CX+oA+NiOPHDnCK1eusFH9\n2nRooFMDzTZVT/1d2p/i50e7EXRYFFrtRp2LYedOarVqsWadOkS3bsTWrURJib79pJkJJBMw0WkB\n1z4KvtoPtBtBi0lgbKiBYWEaNRVU7SYGh1momiQaXH5UJYmJ4WCsG4ypGOEV0P3796ckGajJoPMX\nRuPhAKMDAlhYWMgdO3ZQlVS2rS54lcT6EaBdA/Nb62REsaEaq1WpQqNspN1s55TJU35zvzu17cQ0\npDFNTGN6err3Gtq2batvO0GhAIGjMdq7NZSABDZDM/ZDPzrgYAM0YCpSvd8PwACaYaYBBg7EQI7F\nWEZo/uzWDVy7FmzZEqxXr2a5oLi+vfrSBRc7oiPvw31URZXvvPMOLRaRS5aA27frW0QWC+jnB3br\nBkaGi6xkDGc/9GOGmMZ6dcFatUCHQ18tPPCArkQGDx5AkkxOjuLQoWB4OPjNN/o20333gbVqJTIr\nK5Ht2zfh3r177/RVuSX+F+TL7caAe6QkbsUd8X98En8yPB7PXSciux1WrFjBVq1alTvm6+vLU6dO\n8dChQ5w7dy4XL17MS5cu/aZtYWEhW7VqxYCAAIaEhDAhIYE1atTgsGHD2KNHDy5cuJChoaG3zevv\n8XhoMtkJ4UHCkkGobalp1bwpzD0eDw2KQly54t1wEHNyaDeZ+OGHH7J9+/Zs164dP//8c0oGgVfn\nl82yO9U2c/78+ST1vW7VYOA7AEVfX50jgiR276ZJ0RPrHZ8JPnifgea66fp3n35KZ1QUERxMHD9O\nlJRQ7t+fotVO4AdaTbW4NE+3c2RX1lcvI5rr5y5ZDDZPAQ0P9qTq8mXXbAOfaq97I9kUMDMGjK9W\nzSugR4wYQUUS6VT1FB5FAEcDDAfoALxR9UuXLqVZFdgsGRzSVOesaJJQNuZvngKtRoEJtgTaTXYu\neXXJb+75yZMnGR4UTiOMlCWZNVJrsE7tOpRlmXVRl4MwiGaYWRVVOQAD2A7tqEHjQAxkYzRmnY/l\n6QAAIABJREFUGMIYhzjKkGmDjXVQh/7wpz/8KUL05l8ajuEMUO202cDKsZHeicNNqAaVj+ARr6KJ\nRzxTU1IpCGB6OlilCjhwINiuDVgLtRiOcIYj3Nt/JS2U8+bpdefNK1spdOsGDhmi5/ZasuRVBgYK\nHDxYVx5GI5ienvin0aWS/x1KYt++fVRVlV27dr3l97cbA+5RqvBDAMZCtx1UADAGwPd3e6L/w+9D\nEITbRi0DwNdff40VK1Zg165df9hXxYoVsXXrVmzatAlXr17FZ599BgA4ePAgatSogU8//RRLly5F\nWlqaN6jpJp599lncuHEDR48exZEjR5CSkoI9e/bg0qVLyMjIwMSJE1FYWIiwsLBbnvvGjRu4VnwN\niP4KmJsLDIrBVRzD999/7x1nVsOGEPLygDNngE2bYFy9GvWKirDlww+xYMECvP766xg/dhgkkaj4\nKNB2GvDWNuD0RcGbTtpqtcKqabAA8L92DahdWw+Sa9QIDaoCLVOBYF/ghe4luP7xP4HCQuDYMZw7\nexaBogipYkVIFgsSd+1CiE8ogBMgbbh6A/jhHLDzOGA1AQ8k6uMSRaBlCmBcsRTtKl/A4j4lGNkC\nWPsYIMmArwX4bu8e7NixA6dPn8bbb6xG40QDLhXrL9kY6L7kbwBYAaBvh45o0rAJVry6Ak9Pno4v\nT/hh2xEBrVKBimXZ3qHKgEIVrS62QudrndGndx+MGT0Cnds1w1NPPo4bN27AYrEgLS0NHnjQqbgT\ntC818GOiYlFFGGCAD3zQGZ2xH/vxsvQy3pHegQwZ67AOH+ADXMEVEEQe8tACLbAVW3HecB4VEitA\ngoQP8AFu4AZ+wA84W3gVVS+m48DeY/jmm2/K/fYejwcslUEEcUY9jp27voTVCpw6BaxeDUyfDpz7\nSUYgAtERHXEGZ7BKXoX3pfdxtOQ0Vq9WcPGintH9JqpXB777bgd+/vlndO7cBRMmvIT160Ph5xeM\np5+ego8//hpGo/EP34t7DZJYuXIl8vPzsXjxYm/6+HuBvLw81KhR45aeX/8J+EKPiv669DMd5V1U\n/xO4hzr674fHH3+cISEhbN26NQMCAm7LRnUTBQUFtFqtDAsLo81mo8Ph4FtvvcWsrCwuWrTIW69r\n164cM2YMSXLXrl0sKChgfHy8N1MnSW7atIkhISHe8rFjx2g0Gtm3b18uXLiQhYWF/Omnn+jxeHjk\nyBFmZaRQMoDmiEDio4/0uWDzFuWM82fPnqXm40OjqtJtNnMdwGcADs7L47Fjx2g1CXykicSTs/Wt\nH7sGuqxgqNvJy5cve/t56623aFUUVgI4HWCuLLMuwPgwgSWLy/b4JVkkRoykpBrpp0n0sYgcM2YM\nL168yE2bNpXmqDpHg9iMFiP4eFs9DXivLLB3tr6KuDofTI/WbQ8jm5fN9PdNAQPt+t+fjAN9rArN\nJoVJFSQG+YAOs8Zgu522UmP4zW2npwGGI4it0IpBWhBHjhjJhKoxVGXdQD67p052VDUEdEhGDsZg\n5iOfNqOBbWupXPQw+ECqiS2aNmCX9l0Yb4ynFVbmIc87k09BCuujPgtQwI7oSJNgYpQtiiEBIezQ\noQMtJgtlQaYEiUMwxNuuOqrT1+pLTdUoG2Rq0ChAoAqVmchkTWM8LZrAoCBbuTxjFSMq0gwz4xHP\nWMQyJgacM0c3cEdHg5oG1qkDujQLR2Ik85HPCloFdu/enRMnTuQXX3zB++6rRYcDbNRIN1wfOgSG\nhYHh4Qr9/DSuW7fud5/9L7/8km3aNGSjRrU4d+7sf4k+9Hby5Y/pSwcyISGB+fn5TEtLY9euXe8J\nfemyZcvYvn17FhQU/CUrid+DCYDrFsddpd/9J/Gn3/i/K/bv30+Xy+X1Gvn+++9pt9v5888/37L+\npk2bGBkZ6a2/cOFCxsbqrG+VK5dxTq9cuZIWi4WyLDMoKIg2m409e/ZkVFQUW7ZsyZKSEno8Hg4a\nNIiVK1f29n/hwgXKsswZM2YwIiKCJpOJDoeD0dHRrBwdxifaGXhxni7cTb4W4sQJKl278vnnyydZ\n69+7NxsZDDwJ8BuALlHkQw89xNjYWCqSwOLFZYK4Qxo4oR3oazPyzTffLNfP8OHD+VipSykBnoS+\nPdMoWePolgJdNjBaiGSMEM1IP4mbRoPvDNNpQyWLhYLZTMFmp8lUgaoEvjUUrFNJT/OdFqVvOfla\ndMHdKhV020GjpHtUffu0rjhSIsoyx+Y2ACVRNz772VWOGTWMV69epQ3gW79QEv0AZiGTBShgLnLp\nsDgY4Gti9UhwQnv9XPXiwIfrg75GPUleLGIZ7Ft2bwoXgoF+JjosDtZHfUYjmk442QEdWB/1KUNm\nFKJYAzWoQvVyOzQQGzA9NZ3hgfoWlQKFD+JBr5Lwgx9DEcohGMJ0pLMaqlGBwsfwGGsa49m0ocTD\nh8FNm8CAABO3bt3KWTNn0Wlysh7qMQIRlAzg8OG67WH3bn3b6LPP9O0hk2RiDa0GoyxRzE7P5o0b\nN7y/59mzZ6lICmNNYZQNAhUFHDZMb//pp6Cvr/m2tLu7du2i02nm7Nm63aRqVfO/RBF6K/mi05fG\nsE2bNszNzaXT6bwFfamP15549epVhoWF3YK+dBhdLheDg4N/4y58J7hw4QJjYmJ44sQJjhs37j+u\nJF4C0OYWx1sBeOHPPNG/gLu+uf+t2LRpEzMzy+fEj46O/g2hyU3MmjWLDz1URoZSVFREURRZUlLC\nxo0bs1mzZty2bRudTie//vprejweTp06lRUqVOCxY8d44cIFBgcHMyQkhPHx8axcuTL9/f35/PPP\n85NPPmH9+vXZo0cP7ty5k/7+/l5u5IkTJ9Kslk+znZmkET160BYQwOPHj5e7zrFjxzLM3592o5GB\nNhtdTicbNGjAZcuWUTMp/P45vY/ixToRz6KHQYsExmgax5VSspJ6iuoYTWNuaXxEEsCE6GjOmTOH\n+WPHsknDJozSohhusvPtoWXX9tKDoLVShG64XrGCqsNBt5+Rnlf1lUJBG52fulcW2Kcu6G/T23VO\nBwVk0mrUDdsJEugngaE++opCEiW6bAI/LdA9oBIiJD42eBBtNn9aoPBx6JlkLTB4OSIGYAB9LQau\nfgQc1Agc2LDsOse3Fhim+rEGalCCxOgg2XuPSxaDbl+FRtXImMgYpiSk0CgZaYWVVWOrsmWzlkyK\nT2J0VDTTke5VAv3QjypUNkIjtkM7mmGmBRbWRm1WQRUaYWRXdGWWXItx1lCaZANDEUo77LRqBh4+\nXGYvGDVK4Lhx+TTJenR2D/SgBt2dtmplgZmZ5d1do6JMXLt2LefOnctVq1aVi/y/iScKnqDL7GJl\nsTJTU8q3j4gw3zYrwZgxIzlihOCt++WXYMWKrrtOF34r+TJ+/Hj27t3bW16+fHm591KnL40q16Zm\nzZq/oi+dwIyMDB46dIi7du1ibGwslyz5rY3p9zBw4EBOmjSJJP8WK4mvf+e73X/mif4F3NWN/bvi\nwoUL7NmzJytWrMjMzMxbkoecOnWKTqfTy/GwZs0aut3u286mNmzYwOjoaG8agpUrVzImJoYk+fTT\nTzMuLo4Gg4GNGzf2tvF4PFQUhU6nkzVr1uSjjz7KoKAgTpkyhdeuXeOOHTv4wAMPsFKlSqxSpQqv\nX7/ORYsWsWPHjt4+rly5QtkAHp1RNssN8zcwLTv7lgotPT2dmzdv9pbnzZvHnJwckuSMaVMZ5jJy\nSFM9WC0pHPTRQFkE/WVQNRi8M7aSkhLGBAWxEcBPAU4BGGC1evmwS0pKOGrkKLpsMl/NLRO+T3cA\njempxMWLBElz+/YMD3byyQ4GzuoOJkfocRnFi8HWqWCkPzi1i85JrSnRlNCC/vClEQJ9ZZ2s6Kn2\n+gpkTq+y80zporvA+tsEqrK+CgHq0wALm6AJu6IrI7QIRoU6uXEUeGo2GOPWx12vskhfxUQnnGyH\ndozSomiU9NVN3Srg/dX07a+EhIRyHk1Wi5UXL17kwYMH+fjjj7Ndu3YMNYZyJEbq7q9CBEWIFCEy\nAhG0wkojjBQhUoJEBQqDNDvbtJC4ciXYsrlAiyayAirQxyJx06Yyod2pk5EtW7SkCJEjMIJRiGIr\ntOJYjGWMMZRmM7h/v173q69Ah8PICROe5IABfblgwYLbbsds3LiRI0aMoMMhccsWcNQosH170GYz\n3vbZHzt2NIcOFb3XtnUr6OMj0G5X+dxzk7zp7f8It5IvgwYN4rPPlnG2b9++vdwKu7CwkJUqVeLT\nTz/N48eP84UXXmBoaGi5INLMzEx+8MEH3vIrr7zCbt26/eH13MS2bdsYFxfnXXn9HVYSt4p0vpPv\n/grc8Y39O6NFixbMycnhnj17uHjxYvr7+9+Sqeq9996j0+mkw+FgcHAwFy5cyOnTp3PZsmXlluo3\nMXToULpcLlavXp2BgYFe5fPxxx8zNDSUOTk5rFChgvdl2759O202G4uLi/nII4/Q19eXNptOkflL\nnDx5koGBgXz++ec5Z84choWFeW0EX3zxBS1mI0P8VebdL7BGrJntWjW9rRBo2rQpX3zxRW952LBh\nHDRokLe8ceNG9u7dm8FuP2oKOP8hfZXy4Rg9p9IXX3xBj8fDNm260gDw6i+2choCXva8H3/8kW6H\ng32h51h6piM4rrUeBZ1eVaFWKZI4f55K9eqcO3cuK4S7qCgCLb4arRYDfS1gagW9XWI4GBMIWtQq\n1BQXgxwCTbIeDHfTC6tffZ3ZjkvAi/P07zaN1sv/fFw/r0kOJLCFCmrRbvTlpKcncdaM6YyL0PjB\nKHBZf32MMmSqUFkTNZmHPEqQ6GMGl+SCL/cB7SqoiSIbNGjgVRK5ubl0u9184flZNBoVBrkDWaVK\nFaqqSqNspEWx0AwzXXBRhEgBAiVIzEQmE5FIBxxUoTLABRYX68K2uBgM8Bf4EB5iFTWSVgsYEqK7\nqlotBjqtTgYjmHGIYzCCmYMcFqCA+chnjBhFzQRWq6bQ19fEpKQYPvCAkVOngqmpGgcOvD1954QJ\nBXQ6ZZpMYP/+4KRJoMulcsWK5besv2/fPvr7W/jMMwKXLgUjI8Hnn9djMlwumYpioMkk8b770m67\nXUveWsDq9KUR3LFjB0+fPs1mzZqVe17Jm/Sl9RkQEMDMzMxb0Jc249y5c73lkSNHcsCAAbe9jl9j\n2rRpNJvNDAwMZGBgIC0WC00mE1NSUu5oDDeP/5mCeAuAmrc4XqP0u/8k7vjG/l1RWFhIWZZ5/fp1\n77HOnTt7XTx/jeLiYv70009cunQpAwICmJuby8zMTDZo0KDckv3TTz9lfHw8bTYba9asyT179pTr\nZ+bMmVRVlWazmS6Xi+Ex4TRpJi5atIh79uzh6NGjaTQa+corr9zyOnbu3MnGjRszPj6eCQkJDA0N\nZePGjel0Ovn666+zX79+rFatGpcvX/67cRk6laOTubm5zMnJYUhICI8dO/abel999RV9LWUzcy4B\n06IFb/I/TYunAQaeLlUQ1wCGGwz0MRvYt3cOn3rqKSbKMttpGjsaDHSpYJvq4M5n9L6a1ZCIypUp\n+Phw+fLlVCwWnarU4yHeeYe+NtEb8X3lFV3Iq5LAiR30Yx+OASP8ywfDmVUwrx7Yq46eILDctVcE\nQ3wNBAwUIDHAL4TFxcX0eDycOWMa01Iqs06tBK5atYp2q52KJHpZ4hwmgQVtwAoBMk2KyJhgmb4G\n0Gw2Mzc3l8OHD2dMxRiaVBNNqsSYqFDm5+ezoKCAXbp0YWhoKE0GE0XofdZFXcYilkYY2RqtWYAC\nJiGJNtgY7BZZUlIW1ewOEJhkrMQG2RL37QPXrwetFrA5mrMxGtMII+MRTw0a/eDHPujDHuhBM8wM\ndAby3Xff5Zo1a1itmsWrfM6dAy0WuVzyvYsXL/LHH3/kuXPnaLEoHDIEzMsrW7ls2AAmJkbe9rna\nuXMnu3ZtTbsdfPXVsnYtW4LPPacrvH79ZHbo8MBt+7idfJk9ezbdbjftdjt79+79L9KXOpmXl8ee\nPXsyODj4ruhLr169ylOnTvHUqVM8efIkhwwZwrZt2/LMmTN3PAb8yUqiBnTSnwIAzaCn8B5feizt\nzzzRv4C7+nH+Uzhx4gS///77WwrLkpISmkwmb8ZLj8fD7OxsrlixghcvXuShQ4d+s0rweDx0uVze\n9BolJSXMyMjgihUrvOfz9/fnqlWrePr0aY4aNYo1atTgmTNn2KxZM6qqyqCgIC5ZsoRNWjehGC8S\n00DhfoGVkipRc2qUHpQo1hMZVS2KFy5c+N3xeTwerlmzhv7+/mzYsCG7dOlCp9PJzp07c9iwYb9Z\nifwaBw4c4OTJkzl9+nSeOnXqlnUuXrxIiybzYKmN4vxLYLC/id988w1nzJhBo/FhymjJWIh8EWBF\nReD91cA3HgNz7xepGUFEhBMvvkjpwQfL5VXiEnBkCxCNGxGTJ9MZEkGoRuL773XR8vnnrOCWvHU9\nr4JBPvosf2YOWClEpiyJVBSZnWqBB5/T2eJMMhgJ0AKRqiRw77PwZnH1s+ieWr3QiyMwghXUCnx2\n0rO/GfekSRMYFAQ+/TTYoqmBYZof/VUjLSaJnTt35vDhw1k7M51GRaYsyFQllbIoM0FKYBKSGOgw\nMDOjLLBuyJAhlGWZCUigDNkbzzAO4xiCELoEF82SmbJBpkky0ayB3bqC774Ldu0CWjSBZg08eLBM\n8I4cAWajDgtQwFCEMkqJYrKaTIuqR4WH+Iewf15/72rznXfeYd26Nm/7khLQ6TR6uc9HjnyUmibT\nz8/IpKRYBgWZOGwYOH582Tm3bwdjY4P+8Ll0u324fr3e5swZMDgY/OQTvXzwIBgW5nfb9vdSvvw7\n9KW/RkFBwW23q243BtyDYLoAAI8DWF36eRy39nj6q/Fv3dx7jeLiYubk5Hg5pdPT02+5vH3mmWcY\nExPDZ555hu3atWNqaionTJhATdO8wWw7duzg5cuX2blzZ6qqSlEUy60++vbt6/UcWrVqFZs3b+79\nzuPx0Gq1Mjg4mGazmVFRUczJyaHD4aBskYkLIAiiGIQviDWlZYJqR5WTJ0/29vN7s6aff/6ZL7/8\nMvv27cuAgABOnTqVQ4cOpdvt9irBU6dOcfny5Vy7du1dz8DmzJ5Ft1NjlywLo4LNfHSQzof9ySef\n0GQKJjCfgJlGNKBJMfD6gjKhXiUExJIlXtOnFhrA5in63v/W8aDdqREff0z06ElRrkpBUqhVqEDM\nn09DXh41Vfc22vssmN9K324a2wpUFZnJSUkcPnw4e/ToQUWRaZdkhqk+dMNNp5+LimKhqvjSrOor\nCB8zWNkN2hWRiUo0I01+jFFCmFkzk0ePHuXFixf5z3/+k3v37qWmKd7UFR4PmJYs0wADo6KivIJ/\n3LhxNIgGtkEbBiDAa5iuhVo0KQb62k3s378/x44dy+TkZCqywiEYQhEix2Kst34IQmg1W5mbm8uh\nQ4cyKjKKqqzSX7XSYRdYUQ1hDGJot4jcvPkX9oj2BjZAA+Yjn6HmUA4cOJCzZs3iiRMnbvk7njt3\njqGhfpw2TeDOneCAATIzMhLp8Xi4cuVKVq1q5pkz+ngHDzbQ6TRywACBLhf4xhu6ITo93cQxY4bd\n9lk5ffo0mzbNotEoUdMEVq+u0ddXZuXKBu/K6NVXwVq1qt62j7+7fLkT3G4MuMcJ/v5O+Itv+d1h\nxowZzM7O5pUrV1hSUsLc3Fz26NHjlnVff/11Pvroo5w0aRLffPNN+vj48NChQyR1Y25ERAT79u3L\nNm3a8Pz588zIyOCAAQN45coVfvbZZ/T39/e62W3cuJHVqlXzbj8dPnyYZrOZ48aNY6vOrahWUYme\noCHAQNEqEiVlSgF2EPt/UX4cHDpiKDdv3szg4GBKksTw8HBmZGQwMjKSLVu2LJePiSQTExPLZYcd\nNGgQ8/PzuWvXLrrdbrZo0YIZGRlMTU29IwPi2bNnuXz5cq5YsYIfffQRFyxY4DXgk/qWXULVGPqY\nwQA7qKkijTLKKYnKwSDWrPEqCaltW4YE6uk0zKpA1K5HNGpJwWwl8BJlGJklgjGKxLaSxBoAK6h6\nVtjoQJ3zOtIFiqLAUaNGeQV2YmICFYiMMwj0K/V8MhptfO6551hNUdgHYIYAdgSoSeCIB3QPqJ5Z\net8OgDZBZJA5iDajjZIksLAQ/Oc/weFDwEoxoAEi/Xx8OHbsWBYUFHDQoEEURZHRiKIKlc3RnA2E\nBnSYHZw65VlqJp1nQxAE2mQbTTCxF3oxGtFMQhIHYiDboi0lUSpn1+jXrx9tio1GQd9CCkAAJUiU\nYKDNBubng506gWaTwGxkM84Yx0CfQJqNZgb6BXoTKh47doxPPvkk88fme5/RvXv3slGjDMbGBrFz\n5xY8c+YM169fz7i4EMbHg6tWlc32g4IczMhIoKIY6O8vMyYmkOPGjfzdzAQNGqRz4ECZ587p+Z/s\ndoVr1qxh7drJrFHDwtatrXS5rOUyJP8af3f5cie43Rjwf0ri74HevXtzzpw53vLnn3/OpKSkP2zX\nqlUrtm/fniS9hD8Gg4EhISFe28KpU6cYFxdHSZIYHBzM1157zdteT6PcjHXq1OHw4cMZGhrKgIAA\nbt26leYKZpYG1hJHQSigoY+B+BbEJFCwCRRaCsR5EDtAY6iRK1eupL+/P999911euHCBAQEBnDx5\nMvfu3cuRI0cyMTGx3AtbqVIlbtu2zVseP348+/fvz4YNG3LWrFnecbVu3bqcm+6tcPToUYa7XGxq\ntbKR1cqKQUG/WZ5PfGoCm6aaWLhQVwj9GhhoNQmsX1XimsFg33qgZlWI++8nPvmEmDWLFpeLa9eu\npdHoS2AogW5UFF82btyMilKPJlnhyObg2JagVQErAjwPPe6hTXVdGV2cB/rZJPbt29c7ow8ODmKM\nANaO0OMn3h8J2jWBFSpUYCDAWAW8PxpsGqfbLA5MLXNj9TODiwDOAGiHwofxMH2sCrOyQH8jOK7U\nbdYogC5ZZnBgINPSatDp0DiqhUC/0jiOapUqMadzjpfU59y5c2zetDkzS+MxOqADTTAxHnrgnSSI\ntEkKIxDB+Grx5Tyk3IqbGjSGIYwKFCYjmU3QhDaDjXaLhQF+Lt5f/362bd2WwUEuBhh92R3dmYMc\n+ph8uGzZMlo1K30t8v9j773jo6qz//8zd+bOzJ3e03shoYUaIBBKaAGkdwHpBOkIiNREFBSl2EHA\nQlOaq4gVsaDgAiIWsGEDwVVBEBakBJLn748bJmSB3XVX3P1+fnsej3kkc+eWeb9n5pz7Pue8Xi/c\ndhNOu8qbb755xef85ptvEgpprFghrFsnxMXpf5ctE3Jzfxtl/sWLFzGZFMrUdAFh0CAbixcvpri4\nmBdeeIE1a9Zcc6Vzyf7b/cs/Y9cag/wvSPx32Jw5c+jcuXPYgd5+++1069btHx7Xs2dP4uPj6d27\nN1arFbfbjdVqJT4+nvXr14f36927N16vl3Xr1l1R77hw4QLLly/nzjvvZMOGDTidTp566ilcrVxc\ntk5Ai9TwxnjxJnhp3LYxH330EQmZCYgqGB1GHln8CA899BCxsbHk5eUxduxY6tWrF75OaWkp8fHx\nFXrWx48fT/Xq1dmxYwfPPvtsGGQXDAaZNGlSmOvnwQcfxOXyhtNOhw4dYvXq1WzatClchxnQowcz\njcZy8JnRSEpsAg0atOTuu+dx8eJF+vXuzOPDyusL2wsFlxaHarwNl1Ybi8OCHDqE3HorUrcuSplS\nX4cOHbDb7VitVoLBSFatWs2ZM2eIDvmYf2P5+RYNFDpZhA9EiPUIb00XGqTpr60aIXidFrKz65KY\nEIfVopJiFfbMLj/+7l6CXRPiQkJBXvn22T2E7vX0/888oXddHSob52Ax0kAakKlk4jMpbLysa6tA\nETQRjAYdFf7WdP0cY1rrQU0zC8uXL+epp54iJS6F6EA0mUmZdJSO4dRSW2mLUYzYzTpIcWFfIcYj\nmFWV1JRU6tSog81kI07iqCW1aC/tyZCMCsR/brsb0NN9Pp+VO+/UVxeapndk2cRGfGw8HpeBl14S\nvvpKyM1RSI4vryWUlJTw4IP3kZ4ewSOPlDv1P/1JiI83EhXlqQBE+2estLQUr9fGjh068O6DD4QG\nDWwVfjugK0F+9913V+0MhP8Fif8r9gdP+W+zs2fPkpeXR0ZGBtnZ2aSmpv5DSUbQ76rcbjf5+fmc\nOHGC/fv3ExMTQ0xMDE6nk2HDhtGuXTuSkpJITk6mdu3aYZnRa9mUKVNIS0tDdavIi4KcEwz3GYhN\nj6VatWoVNCYeeOABKlWqRHp6Ot27d8fv97Ns2TJefvllqlatSiAQ4LPPPmP//v2cOHECr9dbIeW0\nfPlyatSoQc2aNWnUqBFDhw4lMjKSWbNm0bZtW7Kzszl06BBpaTWwWAJ88cUX/PnPf8bhCOJwdMfh\nqEedOk04d+4crerVCyOUt4ngNJkIBkNYLE4slgT69RvG7Dtup2O2xoUV+kpidCtBM7co86k/IlYH\n8tNPyP79yLFj2FNTGTp0KFWqVGH69OnMmDGDrKwsxo8fz7PPPovfqbD6MizFs+OFupowUHSg3NHF\nQqRHWDtaOPuEMPkGXZhINQoOo5GgVXhhYvnxN7cQrBYj9WvrYMBL29+YqmtMLBuqI7YbmIVPRJik\nCJlGwShGFFFwifDeZUHiXhE0o+AwlwsRXVghNEwXnhopZPoFm8mE1+plgAxglIwiyhyFx+RhkAxi\nmAwjIAE8otEkvfz9HLhPR4knJiSiGHT8hKZoTJEpNJNmVJfq4SBxq9yK2WTm/oX34/daWLq03MEv\nWCBUs6WQL/nY7bqYkMMhTJ+uPzRNCa80J0wYRb16Npo1Ex58sPwc69YJ2dkZvxkAd8kmTZqAwyFU\nqaITAMbFuSvUwNavX4fbrREVZSM62suf//znK87x3+5f/hm71hjkdw4Sm/7O4/nf80JAuCIFAAAg\nAElEQVT/gv3BU/7b7eLFi+zYsYOtW7dW4Bu6mp09e5ahQ4cSDAbxeDx8+OGH4dcWLFhAbm4uTqeT\nzMxMOnbsiN/v57XXXuPMmTMEAgGaNWtG165d2bp16xXnLi0tZePGjQwYMAB3tBvFqJBaI5WpU6fS\ntGlTmjdvzs8//8wXX3xBSkoKzzzzDKdPn8br9TJ16tTwebZv347RaUTxK6gRKvaQnUGDBlW41saN\nG6lXrx4lJSWUlJRgt9vD9ZXS0lJq166NophQ1QLMZicnT56kUqU6iKwt84MlaFobHn74YYqmTqW1\npnFahCiT3tFTVFTE5MmTcThCKIreOtm6eSOSouxUTnQSF+VH06oishmRx1GsdjSrQjBgxmI24AkG\nyMvLo3v37uHUSp8+fWjYsCGVUmKY0UlIiRDeniG8WyTEBwwYTEbEYsFmcdG5jpm7e+oCQ4pBrzu0\nFuG8CJtFMIsBh1W4vaswLM+A2WhAUawoBp2D6dijehttuxp6h1RHizDEoK8OXKqO9L69q65Q1026\nUU9qUl2EZWWpKJ9B8Jnt9JE+uMwmOtbWsRtta+g0IRGqYBIhWrOTogVoa2jNMBlGyBsiOSYZq1hp\nIA2wiYGudcuDxOnHyqhERGjnchGpacQHo2ikNiJDMrCIhY7SkaEylBRJwWP3EG2NJuiy8Oyz5Q5+\n5Uoh4DST6ohkzhx92/79gtutU36PGiUEgzri2mIx8fPP+h1/ICA88ojw+OOC32+kbdsWvP/++7/p\n91ZaWsrw4f3xeg3cfbd+7XPnhCZNbGFswsGDB/H7Nfbs0V9/7jkhKsrD+fPnK5zr/wX/8o/sWmOQ\n3zlINP0Hj/+k/cFTfn1t1KhRtG/fnsOHD1OnTp0KBHuDBg3i9ttvZ/PmzXi9Xrxeb7jo9vTTTxMK\nhVizZg2PPfYYwWCQ7du3X/UaJ06cYEbRDDr26ojLpauX9evXL5x2sVqtzJ8/n9LSUkaNGoXVamXc\nuHF88sknDB01lMQqiUg9QS7o3VCGngb6DanYfldcXEzTpk3Jz89n6tSpGI3GCj/ATp06Y7VmYbOF\nWLpUx2F4PNGIHKT8hrmQKVOmcf78eQb07IlZUTAYDBQWFoYde5UqdTEYFBYsWMDbb7/Nxx9/zO7d\nuzl37hx33XUPvphEVF8Am1UJU3Hsm6uDz7xOJ9nZ2RQWFlJYWEjDhg0ZMGAAIb+Tww8KSwbr6Ok4\nn2CKikCOH0c++QQlFCLg85EcF8RrMfKMCG+KkCHCkrI330gsiMzBqExAJBOrVSMvL4/q1atjtZhQ\njTpCulu2YFN0UsKHRKhh1lNTl5z2Y0MFr2ZggAzAIQaqiOAXwasJCZYABVLAzXIzfvFjVASHUYhS\nhf4iuKy6zOkbU4VKQZU6hhrUyKzBuXPncGpOukgXLEa9FffJAp2K/IaagsMsfFU2jsMi2EWIDkTj\ntrjJlVySJZlIiaSqVMWpOhkkg3Aa7CTEGNm6VXj9dSEqZMQqVqyqwokTuiNeuFDHKFwKJK+9JiQl\nBTCbFU6e1Ldt2yakpip4PAYGDxbuvlsIBGzs3LkTgJ07d9K8eUOiotzUrJnK5s2br/h+/+lPfyIr\ny05CQjnCG/QW4sqVU5g9+07Wr19Py5blLbggxMbawjcyl+z/gn+51hjkf+mm/zctOTk5zCq5Y8cO\n3G43ffr0oXXr1mRkZHD8+HE+/PBDQqEQlSpV4t577+X48eNUr169AuHdwoULGTJkCFu2bKFZs2Zk\nZGQwfPhwfvjhB9Ky0jD1NSEPCWqmyq1lbYR33HEHHo+H5ORkFi9ezDPPPEPVqlX54IMP8Hg8GB1G\n5HZB7hckKMjzZVWNlwU1oF6RFjh37pzOmzRzJjk5OQwaNIivv/6adevW4ff7WbRoUQUGzTZtuqGq\nIxG5iMh32GwpvPTSSxXOl5CQQJcuXSgqKmLChAnYbHYUJQartQCLJYJu3XqyY8cOALr374+1Uyfk\n1Vfx+sxXANlMBiEyMpK4uDhCoRCqquKx2WjZLIceOVb+8pDwzkzBZTMga9eWu5PevUiI8RLtt9HR\nIBQZhDFGvaicV4b4jhTBoibjtkVgNpsZPHhwOLBVzsykfooerBpn6E55cI7QL1t3/k8WlL/PTRN0\nEaNIxcVMERYbhFSPsGaUXkNwqEYizU6qxOjSqTaz4BUjLpPCgr7l53lruuC1K7z88ssAPPPMMzis\nDlyasPtOndqjcoyQEBB8l6W1EKGGWIhT4nDb3XiMHgqkgDEyhhRbCjH+GPpKX2IllqqGyiS5fCS5\nfNSQLBziwOkwcOONwoQJQs+eOqXGJad88KDgcgmRkUaaNFHYvFmYPduAzSYkJupBpbRUT0H16dOJ\nmTMn4/Mp1K+vH6eq+qN169wKehZ33303Y8Yo5OTo+hO//iqcOiXUqqXXSlq1MpGWFkdkpMaRI/p7\n2bdPcLutV6z0/y/4l2uNQa5TkEgXkQ0i8pno2hLfyn9eT+IPnvLra7Vq1apAgdy+fXtq1qyJ0+lk\n7dq17N69m5ycHDweD7Nnz6ZOnTpYrVb8fn9YzAf01FTLli1xuVxER0dz55130qlTJwKBALbGNqS0\nzMH/JJgsJi5evMiGDRuoUqUKBQUFZGZmYjKZmDhxImfPnsUd40aKLit3PytIE0FKBWWoQkxqzDW5\nY0BfvVxC+tatW/eqq5xPP/2U6OhkRGwoisqcOfdesc8HH3xAMBjE6/WhqiomkxuRU6gyHb9odBIh\nTtO4c+ZMVJsNOXYMOXMGi0sLF5L/8pBeV/jTOMHvMhPp9TJahF9F2CWCX9Po2qkNAa+DlIRIYlJT\nkA0bdNf2yit43UZenSy8dpuOuh7QSJh3oxB0CnajkGy1Eul30KKawvoxgs2qMm7cuHCQaJjTAKMi\n2CK9ODWp4Mw71RZCLv3uf3uhzs3ULVsnNNwlQmVN18B+farQvrZKhEelRdVyMsVpHYUIiwOb0cSM\ny6RQN94i1KqmB91NmzZx8uRJvvnmG/weGy+WrbC+mKfrhtuMRjaXBYjtIrhFpY/0IShBUiUVVVS8\nTi8Tx03kqaeewmfzkSM5mMVMM2lGc2mO2WCmb9++uFwK3bvrd/Eej86ftGOH8MUXQny8it2ukplp\nJjraSFpaEL9f4ckn9dRT1arCAw8IK1YILVo0IDbWxtGjwpIlQu3awrFjwvnzQpcuagVKj7Vr1+L1\n6sdnZuoBxesVhgzRj+3UScjPt9O9e0eio220besiGLSxevXKK75v/xf8y7XGINcpSGwXkRYi8rGI\nJIiOwL7jelzoN9gfPOX/vh0+fJjZs2czc+ZM3nvvPZYsWcLs2bPZvn07GzduxOFwMHr0aDp06IDX\n6w2nkbKzs6latSqFhYXs2bOHvLw8oqKiiI2NJTMzk6ioKFatWsXixYtxOp14PB58Ph979+4NXzsz\nMxNTO1O5sz8nKGaFTz75hJo1a9KmTRumlDGrrlq1iqysLBYtWoQp2YTcd1mQeEMwBA04qjgwOAy0\nb98eq9VagcSspKSEF198kccff/yqnPuX29GjRwmFEjGZRiKyAJstgUcfXXrVfc+cOcO+fftYu3Yt\nbnceIgexi5WfyhzbfEN5EdmemYx89x2yfj2aplArUXfCl1I6NzXVsF7WOYUIXZxO1qxZE77e1q1b\nsQUCWAsKcCZF81B//dhHB+ua2qwWFg824LCpREToK5LLMRpD8kwkJSUycuRIevfujceh4nSYMMfG\n4nHoAeeSM181QvCZbAQcQkaU3gnlMAuqKKQb9fbWyjGCxWymXbt2tG3bFs2isrWsu+nNaUJAU4gW\nD25VKOws3N9PV80LeAOkOlPJcGYQFxnHd999x7Zt24gIuEmJcWDXTHi9Kn376kHJJYKjLEC0k3Yk\nSAJe1YvL6cJmszF+/HhKS0t5+eWX6dujLx3bd+SGNjcw8KaBvP7667hcGm3blq8c9u0TXC4LiYlB\nLBaVmjWzGDduHD179sRmM5GRkcLixfrq4c47dc0Jk0nw+RTGjRtHp05Ozp7VdawvL5K/+65Qt256\n+PO67bYJ9OxZrsE9YYJOCHipY6pNG6F/fxtLlizh448/ZuPGjddUV/x/0b/8rV1rDHKdgsQlNti9\nV9l2vSxfdBLBL0Vk8lVe/4On/N+zAwcOEAqFyM7Opm7dulgsFrKzs5k0aRIRERFERERgMplQVZXO\nnTtTv359kpOTyc/Px2w2Y7VaufXWW8P6Ds2bN2fs2LF4vV7MZjMRERH06NGDd955h5iYGDRNq6CT\n3aNHD0wuE/KwILsFpZOC4lDw+Xx06NABv98fbjcsLS1l5MiROBwOpKMgEYI8I8jrgiQKEyZN4PXX\nX8dkMhEdHU1UVBTffPMNoBfrO3XqRM2aNenbty+BQICNGzdec14WLlyIxdLvMl/9HqFQ0t+dy8OH\nD2OxOBFpQ7TYoGwlEGXT74pLV+mIaHtyNOYBAwglJqKahA1jJcxOWynWiqooPClCadlqIsVm45ln\nnqnAg/X555/zwAMP0Kp5E2Z20XWoF/bV8ReHHhCcNhNjxowJE+uZTCZqJQofzNa7nwIuI36Xmarx\nZka3EuxWA/fccw93zZ5FToaVQw/oeInUCANREotZUXBpdhyWWGxmNy7NgMWkp57qpJjp1KlTeGXS\nvn176qebKV4u9KotBE2CRQxsE2GYUehlErqJEK1Eh+VBG0tjGtdvDOhB9/PPPycy0sP77+vO9Px5\noX59IUqNIkMysIudKHMU+fn54aaBmJiYKzQ9Ltmrr75KWpqV4cOFQ4eEhg1VrFYFs9nM66+/jqLo\nYk/hFFzlTOLjY7jjDgNr1ujypocO6amitm2FNm2a4fGY8fn0VcGNN+rpqq5ddY3r5GS92+7EiRP0\n7Nm2AlfTG28INWrof5OThYICIRCwX1F/uJr9N/uXJk2aYLVacTgcOBwOMjIyrrrftcYg/0KQ+Gfk\nS8+JiFFEvhKRUSLSRUTsv/VCv8GMIvKQ6IGisoj0FpHM63i9626jR4+W06dPy88//yyff/65xMTE\nyJEjR2Tu3LmSmJgogwYNkuLiYnnjjTfkzTfflMjISPn5559FURQ5fvy4fPfdd/LGG2/I0qVLZc2a\nNdK5c2d55ZVXZMeOHbJ//35JTEyUpKQkadSokTRs2FDsdrsMGTJEvvzyS3nuuefkhRdekCcfeVI8\nd3vE0tYiGYcyxK26JTIyUj7++GMRERk4cKDs2rVLDAaDPPTQQ7Jq1SqxvGsRuVVE7hORQSKBkoC0\nbN5SCgsLJT09XcaNGycWi0ViY2NFRGTjxo3y/fffy86dO2XlypWyceNGGT58+DXn5cyZM1JSErxs\nS1DOnTt7zf0vXrwoffv2ldjYkOTknJQTNpFhBoPsEJEbaoukR4kYDCLTOoqcO/CDTE9Jkbb5+UJe\nS+m/wiadltglbrxRvj1SKpUqV5YxLpekWa2SYjXJ9xfOyuB+3cXjUMVrt8vK5culUqVKMnr0aHnw\nkSWyeKtDpqw1yKFjIqu2iTz2lkjA5xGfzyciIqFQSCJ8NulQW6TV3SKjnhQ5c84nv54plk8PFcuf\ntnqk+rn6Mv3WGXL2vCI5bYZK+kRFqk62yMGjDvlBjkppaZT89WyaiHhl0g1/lRNLEa9d5PkJIn5n\nRXlbo9EoHxwUCQwRObVXpPFFEYvZLOMUgzxpVOU5RZUXVEXcpW4xiC5xaRKT7P74bTGbjdKhQ564\n3W45deqMJCfr5zSbRWrXNkuz7s3EV98nQWtQfi75WbKyskRERNM0SUpKkr17L79fLDev1ytnzxpk\n/XqR3FxVzOYGMm7crdKtWzfp0qWLWCwW+etf/yoiIoCc/OWkHP/hpMyfb5b580WGDxeJjRX58EOR\n48dFdux4S0pKLsq6dSLffCPy3nsi1auLVK4ssmqViKL8LLVqZUpEhFe+/vqArFxplXPnRC5eFHn0\nUYP89JNNBg50yZkzLvn00xrywguvS2Ji4jW/X/+uQZl86YwZ102+1GAwyMMPPyynTp2SU6dOyWef\nffa7X+NfsWwRcYpInIg8KSJ/kutL8NdARF657PltZY/L7fqF6t/Zjh07hsPhCLf0ffTRR/h8Pjwe\nD59++ikWi4XTp0+zd+9eIiIi6NmzJ/n5+djtdp599lkAvv76azIzM7FYLLjdbpo2bcqiRYvYuHEj\nXq+XyMhI7HY7L730Eo0bN6ZOnTq43W6cTicul4s777wT0LuPVq5cydy5c9m2bRsNGjRg0qRJfP/9\n96xZs4ZQKBTGPZSWltK2XVvEJohJcEW56NChA3Xq1MHj8WAymcjIyGDv3r188MEHNGvWjFAoVIF+\n5Pz58xiNxmuywe7duxebLYDIOkTeR9NaMGzYmGvO5SW65kvMpmPHjsWoKBgNBirHCMXLy9MvcdE6\ngVuzjh312sJnnyGPP45R0xg6dChFRUVMmzaNQCBA0KPy/UP6XX2PbCHBqHcXJUT76NKhNb07daJG\nSgpZmekUDB3E7NmzCXgsqKqJgoICioqKGDx4MG67iRNLhXopgtXkQeRe4iWB6TI9jDNQxYmmxbB9\n+3aSkipjEpWO0pFsU3XcVoW0SMFiEn5erI8lyiN8OV/HbPjdGj169KB79+7YNCu5IrwhQpJZMCqC\nXTPj8bgZPny4Thke4cOqKkyRKQyX4TjtBt5+W79TnzzZRJMmtenVqz19+lg4dEh49VUhGLSxd+9e\nLl68yIL5C/D7/HTs2JGioiKmTp1KfHw8GzZsuOrnU1payo03diItTZ+byzvSqlevzqBBg3C73TRp\n3IT05HRi1VgGyABiI2Lx+1z0uVH47DO9JXbVKp2SJCdHrzEkJAjVq+syqCDcfLPQt69QXKzLnNaq\nJdSsmYnPZyEUstKiRYMKadDfYtfyL/9QvnTYMLLsdmaKUN9up2+XLr+7fGnTpk1ZtmzZP9zvWmOQ\n69zd5Cp7XG/rJroq3iXrK7rG9uX2u0789bT333//iiVhnTp1SEhI4KWXXiIQCLB582Y6dOjAwoUL\nWbNmDY888gi9evUiLy+PCxcukJGRwT333MPJkyd59NFHUVwKqkfF6DGyatUqQF/qa5pGv379mDNn\nDv3792fatGlXpREGPXg5nc4KX+IOHTqEHcDy5cupVasWx48f5+LFixQUFIRFgUpLS8NaFH/5y1+I\niIhg2bJlrFu3Dq/Xy759+ygpKaGwsJDc3Ny/Oz9vvfUWWVm5JCRUY9y4yddEwQKsXLmSWrVqhR3P\nzJkzMRqN7Nixgy4dWlMtyUGPRk4CXlu4o2fGrFlo7dohZ88iv/yCQVGucF6tqhvZfJuOf+heT0iN\n0IWO9s0VhjcXEq3C2yKMVVUSgkH8VivZLhdWk55KcToduGwmXpykB6pYny4sJOLDZXAxTaZRJEWM\nlbEooqFpN3HHHXegqg4iJZX6UpcYr/DLEh0cF+PVEdS/Pq4XpSvH6EXofo0Em9WC02bBaxIcBgM2\nsw6kK10l1E8307Vr1/DYevfujU2zoIgBgxjo3s3AyZPChQs6ZbbJpHDs2DH69etKZKSLKlUSwvMG\nOnfWokWL8Hq9JCUl4fP5GDBgwN91fCUlJaxcuRJVLS/cz5gxg+joaKZNm0blypXxiY820oZpMo3h\nMpyEyAQee+wxnHaFpASlAjX4wYM6zuKrr4Rp04TISOH774VQSK9duN06vmLpUiE3txYHDhwIo6mn\nT59ERkYMtWun8dxzz4Xf4/r164iN9WG1mrjhhmYVfiPHjx+/qoM9duwY6bGxdNU0RpjNBGy2K+VL\nLRZOlOVOz4gQb7NdKV86diwhp5MYr5f5c3+7tGrTpk0JBoMEAgEaNmxYQbjrcruWj5TrFCTqil6P\nOFj2+EhE6lyPC5VZV/kngsSlPvfCwsKrcsL8t9jPP/+M1+sNf1k+/fRTXC4Xdrsdr9dLo0aN8Pl8\n+P1+qlatSpMmTRg8eDAejwe73R7WagDdOVetVxUZL8gBQR4T3JHusJ51amoqdevWpVOnTjz00EM0\naNAgzJH0/PPPk5eXR9WqVenVqxd79uxB07Qwj82FCxfIysritddeA3TsxsKFC8Pj+Pjjj4mJiWH8\n+PG8+OKL4e2rV6+ma9eu4edPPPEEZrMZVVWJi4tj0KBBFRzPv2M7d+7EYrHQu3dvJk2aRHaDBqhx\ncbgiIvjyyy/ZvHkzq1evrlCQPH/+PO26d8fsdmPxeHB7POEc+4gRI3A4HET7TLhtwubbylHMOWm6\n+E/JSsFn0bWzPxO9sPtdmSP4swhem40hA/tTKdbGjM5CnSQVzVwbkSWIzMNt9+EVI0GxoIkVkTux\n23Uwo83WAqv4sItCQa4OaquXphIVchMIBHDazCRGWqmSkUxs0EGWRVgkwvMibBGhelISqTH2cAG8\nTyMTeXl54SCRn59PnRSVeL9eCPc5BLMq2DRhxnRdy2H48AHcd9/CK4Lzq6++itVqJSoqCo/HQ7Vq\n1SqAPK9m+/bt4+mnn2bnzp3Mnz8/7MiSk5NJSkoiISGBZs2a6Y0Xplj6Sl/itDjyW7ekR4825Ofn\n4XV7wwVnED78UOdzukQtHhWl036PGqUHu08+0XW0q1YVPB6FhIQA6elRxMW5SE9X+fBDne48IkLj\nnXfeYc+ePYRCGn/+s94iO2KEyg03NAN0Ua5QyHlVB3t7YSGDVTXc6LBWhEbVq4df//TTT0lxOCo0\nQ9RzuyvKlxYV0dBm41vREfaVbDZWr7yyu+of/QZOnz5NcXExy5cvx+l0XrUAf2kMb775ZgVfKdcp\nSOwVkdzLnjcSvdPpell9qZhumiJXFq9/08T+p23GjBlomkZGRgaaphEbG8v+/fsBXSQoGAwSCoXI\nzc0N36Vt3rwZp9PJokWLsFqt/Pjjj/zwww+oXrW8lRXB0cLB888/z+eff47T6aRy5crh9M6RI0fQ\nNJ0u2uv1EggEuP/++ykqKsLn8zFu3DhSU1OZPn06eXl55Ofnh/mm7rnnHjp16hQ+18KFC0lOTuae\ne+4hMTGRRx55BNBZbBs1ahR+3z/88AOqqpKUlERBQQHz5s0jISGBpUuv3rX0W6xW41oofRTMTgtG\nmw21QQPUypUx2WwkJSdfwUp7uR05coSffvqJmwcPxmY2YzYaMRmNJMREUT3BiEUVTj1W3nE0upUu\nPXp8iWAzCidFeE6EnDIHsEKEJBFsIvTq2JF169ZRUDAMs9mByB2IPILVGsBrsfBUWUCpKwZsRo0x\nY27ltddew26vgiI1yRUh2S3c0tZAjWoZ4XRabm4uNWvWJD4mRHKECacqWBS9C8luFKqnpWFV9SL6\nJeU71WSidu3a1KuXjddh4sM5+orI79ApQ2J9wqJBgs8uJCQYeeABoVUrjY4dW4Q/6wsXLuB0OGnX\ntl14JRAXFxemjr+aLV28mJCm0c3pJMFmY+qECbz11lvce++9PPzwwzidTqZMmRJO8zmdTqplVCMn\npyZNm9pYvVoYNsxM5cqJxMcHGTPGxIMP6iuHe+7Rg8TFi0J8vI6TuATWA2HMGL0rqmdPPVjs2SNs\n3aqr523apO8ze7YwYcIYFixYwMiRlvCxp04JFouRiRPHEAyaGTlSrhokxt58M/MuCwAfiZAZExN+\n/fz582TExXGXonBYhEUGA3F+f0X50mrVeOOyczwuQr/OnX/bj+BvLD8/nwcffPCK7dfykXKdgsQH\nV9l2PbubTCLytYgkiohZRD6UKwvX/9bE/pF25swZQqEQa9euZfv27WzZsgW3282BAwcoLS0lLi6O\n559/ntmzZzNp0qTwcUeOHMFqtQI6BXdCQgK9e/dGVEGOlIWIC3rHUXZ2dphEr3FjvXvlxIkTpNdM\nR6oKSgMFk8tUQTpx5syZjBkzhhdffJHCwkKWLl1a4W7yzJkzYSfVtGlT3G53mIl27969BINBCgsL\nGTRoEElJSXTr1o158+ZRpUoV8vPzufHGG9m/fz8bNmxg1apVxMfH/9152rJlC5MmTWLUqFG88sor\nV6Q0zp8/j2JSdO2LGi7kxRcx+f106dKFCRMm0KhRI2JjY8Myr6NGjarQqQR6y63TbOaICL+IcFoE\nt6aT8uVV0VM7pav0GkDAIYxooXcWBVVhrgiNjUYcIqwSIUaEnaKjk2+wWhlZRlHy8ccf079/AT16\nDKR///5MuKzV9isRolwu4BJjb0+s1hQixUCuIljNFTuYBg4ciN1mISkoBB06gd+Y1jonVMApaGad\nATbGq6eiEgK6nrZBDGSn6OO4pJbnL1P3Wz5cT6kNaVbufM+fFxIT7eHV7tdff43FbAl3bhUVFdG8\nefMwSeWGDetJTAzidmv07HkDhw8fxmk282XZOI+JEK1p4Tbszz77jIiIiAppvuTkZDZv3ozNpnLq\nlP4+SkuFhg2drFy5kttum8jw4f3p2LEtqakKc+YITZoIPp+JiAgd4X1pddG0qTB6tL7CuFx/e9Ei\nYdAg/f/Ro43MnDmdFStWkJtr48UXdfT3K68ITqfC8OEqTzwhZGdfPUi88MILJNps7BXhiAjtNY2x\nf8Nk/O2339Kifn0iXC4aZWVdKV/atCmPXhYkphiNjB52bfnWf8b+W4LEfSLyqJTTcSwSkYUiUqvs\ncT2sjYh8IXpH1ZSrvP5vTewfafv37yc+Pp6nnnqKtLQ0oqOjiY2N5aabbuLXX3/FbDZTUlLCnDlz\ncDgcjB07ll27djF06FBiyu5U7r//fqpUqUJWVhZSVZDKoqOgWwhiF9asWcNXX33FyZMnSUxMZO7c\nudw05CaUvkr5quN2oX7z+uH3tXDhwgp03YcPH2bXrl0VWmeLi4t57bXXGDx4MAMHDgxvP3ToEA6H\ng5tuuolHHnmEmjVrkpeXx5gxY1i7di133XUXrdu0xha04ergQovV0DzaNeeoaE4RlgQLMlIwZQhO\np5FeHTpUCBSlpaXYPDZkryB3m5CUGGIzMioI8JjNZgYMGMD48eNJS0tj+vTpFdT+JoUAACAASURB\nVK7z1VdfEW+3h3+kO0THErw+Vfj+IR2RbTYJFqPgMQjJJuE2EWaJYDcYUJVUjKJgFeGOy37sX4iQ\nHApVuNaxY8do0yaf6iaF18v2e08Ep0Fh7tz5gB4oFi9ejEMzMrKl0LSygbi4OKZNm8bMmTPJql6N\nQc1MlK7SxY4uYTVYrbPARnmE1tWEXbN0Ko83pwsGETRzEi5NeGSgsGWKzu80upV+3IP9hd4NhCqx\nwtq15Q41K8sVpsH4+eefsZgt5NTPobCwkMmTJ+P3+Zk9ezbvvfceoZDGtm3C0aPCTTdZaNmyIYHL\n5gMR6htNvPLKK+HvUVpaWphNuE2bNkRHR5d9j9QK1N7Nm7sqAERLS0tZu3Yt48eP4oYbbkBRdAEi\nl0tvia1fX2jRQi90+/3C6tXl55o6VWjUSA8QMTE+Dh8+zLvvvoumKSQn6wVxTROaNi1fWRw7dvUg\nAfDIgw8S5fHg1jQG33jjvyZfarczUlUZaLEQ4/P9JvnSEydO8Morr3D27FkuXLjAqlWrsNvtFZiY\nL9m1xiDXKUi8JSJvXvb42+f/CftNH85/0k6dOoXFYiEyMpJt27bx7bff0rRpUyIjI8N0261atSIl\nJQWXy0WdOnXw+/04nU6aNWtGs2bNiI6O1sFaViuSIch6QaaWBQpVOHr0KBs2bGDTpk18+umnulRp\n0II8eRkQ7h3BHNT71Z955hlcLhcpKSnExcVRP7c+Fq8FZ5YTs8uMO9qNP97PjFkzKC0t5fPPPycQ\nCLBu3Tr27dtHdnY29erVo7S0VAfkNa6JOIW23dty9OhR3n//fcQiyL6yax8TTCFTBa2JS3by5ElU\nu4r8pWzfs4I9QkjVtAr0HADLVy7HGrJiGmpCiTTj9njCAjwTJkzAZDKFhYAGDhxIVlZWheMvXLhA\nRnw8cxWFn0UXAGolQoxdB59NaidoJiHGYsFUhp9AhB9EqCSCSF+MotFIGtFHFHaI8IQI80SolZYW\nvs4vv/xCWnIM/ZuozO4hhOxCXxG8YkakNRaLi7S0NIYNG8bgAX0oLMNgFC8X6qaasFgs2Gw20qJV\nTizVnXudZOHlW/8GgGfXQYIrhuupsYltdX4qkZuxqnG4NSFoFxyqcG9v4e6eui5GfFBw24Tx4/Sc\nf1GRiYyMOM6ePcuXX35J0OXEqur04RaLBaPRSEZaBiUlJdx9993ccouJS0716FHBbjfhECNrpRyx\nbRPhq6++Cs/Jd999R/PmzQmFQjRs2JAvvvgCgM6dW9O1q5UtW4TCQiPJyZFXyOaWlpYyePCNpKUp\n+P26vvbq1TrDbEGBjqi+VKtwOAzMnCmMH68QCNjp378P/fr15emnn+bDDz/E7Vbp0EHHYaSlCXXr\nKrRpo4bHc+bMtYPE72H/jnzp0aNHqVu3bhg026BBgwoiX5fbtcYg17m76b/JfvOH83vbnj176NKl\nCy1atOC+++67ZpsnQHp6ergNFfQil9frBfQuokAgQFxcXPjO669//StJSUlYLBb8fj9Lly7l1KlT\nLFu2DKPLiNQUDAUGxCO0btOamJgYatSoQXJyMpmZmXz11VeY7CakoSB/FaRYkE6CK+QiNjaWjIwM\n/H4/W7Zs4cUXX8ToMyKHBHlCkEq66JB8KiiZClNm6Ejst99+m5ycHNLT02nVqhUdO3bk2LFjeKO9\nGB42IJ8L6miVGg1rcPDgQSwRlgr6Fa5811WBdd999x1alFZhX2ddIaQpRGdG03tw73D3yccff0zA\n4aCyxUKixULQ5yM1NZXGjRvj8/lISEgIryw6dOhAs2bNrrjeN998Q5PatXFrGgkRETTVNF4WoUAV\nehiFKI+HDz74ALPRyC9lq4SgyURmpUqkpFRGNWnESAIOxYJD06iWkYHNZgt3foFOud6rkTXs0HfO\nEhwWBb+Ycatm6tWrR//+/alduzaRkSGWDil3/lumCEkxPlq2aEbTTAkLKjWupHc5fXav8NFdemqp\ndTV9m0vT02Vum+DWzPTtO5Sbbx7L6BEj6Gm1sq1sfDVMgtMiOMwqtaQWXo+ZjIxounRpxaFDh/T0\nZyBAQBPeu0M4sUSnB3HZDeFU5KOPPkq7drYwsnnbNiE62k2cLY6A2LCLEYeomBTT3+1Uu2Rnzpxh\n4sRRNG6cRd++na9Kqb9r1y6Skuz8+quwZYu+YsjL0zuc0tJ0ZPUrrwjNm9u45557uO22icyYMY2P\nPvqIOnUyqV3bSU6Oi1DIyty55SuN0aP183g8Kvfeq7B1q9C+vfW/Gkz3z9q1xiC/c5DoV/Z3gojc\nctnj0vP/pP3BU17R9u/fTyAQ4OGHH+all16idu3a3H777dfcPy8vr4Jg+YsvvojP5wsrtmVnZ6Mo\nSoVAc+ONN5KSkkK1atUqnCslJYX8/HwGDBhAYWEhmqbh9/upXLkyQ4cOxe1207JlS5zVncgwQTRB\nHLpk6aUusKKiojANx9q1a3F0cujuuasgT1/mrjcJqtfAmIICfvnll/B7+Omnn4iKiqKgoAB7nr18\n/xLB4rNw+PBhAnEBZE3Z9vcFza9dFe168eJFXexojuia2xsEgyZIP0H+JFjidF3v6OhoqqWmhhlX\nS8pqAb169aKwsJDly5cTFRVFrVq1qF+/Ph6Ph127dv3dz/H8+fM0rVuXRg4HgzWNgKaFO7fGDBtG\njs1GtqrSskWLcPCpX78+HrsRVVWZOHEiRUVFjBo1Ck3TwtKqd911F7e0M4Ud/+EHBU3Vu5ISfb5w\nbn7mzJm4XC7iQlbeLdKdf+VoA5WUFHwWH06rXkuI9wuRLsFrEtyqTuhXOVMIefQ6xbf36df5/iHB\nYTWEUxinT5+mUc2aVHM4yHG5iPP76d61O43qNqJX115XdMUcO3YMs6JwS6vyoHXsUT0F9+OPPwLw\n66+/Urt2Bu3a2Rg/3kQopPHUU09RrVI1appr0kJaEGuLZfiw4ezcufOq2u6X7MKFC0yfPomqVeNp\n0KAKr776KqDX4z7++OMw8d6DDz5IWprKs8/qxesPPtAL1TabviLYvFlPLcXHB8Lp0pKSEgoKBtK9\nuxrWtk5NrVizWL5ciI9XmD59Kl27tiYnpwq33jrmf0HiN1hB2d8iESm8yuM/aX/wlFe0O++8k3Hj\nxoWff/rpp3+3MBsfHx9mdp00aRIOh4OioiIee+wx7HY7drudpKQklixZAuh3136/nz59+uD3+8MO\n+sSJE7jdbr766quwVGlcXByapmEymcjOzuaNN97A5XJh8ViQ7YKcEmSzYPPbwnfkCxYsoE+fPoAO\n7rNEWJDDggwUZPZlQeI+we0SHG7BHXJw4MCBCmOuX78+SmpZMbksraTaVE6ePMmsWbNQ7Aomtwmj\nzUgwFLymkMw333xD5XqVMah6QJOgTiKoNlBpkNuAqVOnMmDAAMxmMy9dlveeK8Ito0aFz3PkyBHu\nv/9+7r333nD32D+y4uJi1q5dy6JFiyoUGktKSrh/wQIig0H69etXgQ6jUrSCx6kxbtw4Bg8ejN1u\nJxQK4XK5mDhxIh999BFBr43nJ+h3/vk1VUKq3uEU63aHu5emT5+O0+kkq2oGPoeCSzPQRMlhpswk\nJCHyJI+axiq4zEK8RUj3CjmxQsgsxEXoBdvUiHKHzmohI1oqECkWFxfzzjvvsGLFCtrk5lItIYFB\nvXpVCPqX72s2GmmRXk4euHW6YLdKhSaAX3/9laVLlzJ37tywaNWWLVuIj/ficJhISQnhdluoVcuF\n32+r0DJ9uU2ePI4mTWzs3q3rUQeDNiZOHI/bbSEz00lkpJvbby/C7zdz441CzZp6jcHt1kWFfD6h\ncWOhXj3B7VbCKaxffvmFGjXS8PkMeL0671NxsdCnj9Csmd7RdPSoTgNSpUoq7ds3oXPnFuH27/+0\nf/k97FpjkP+lm/4Yu+uuuxgxYkT4+YcffkhiYuIV+/3000/MmjULVVVZv3498+fP5/bbb6d58+Y8\n8cQTAIRCIRITE7Hb7dhsNgKBQJmsZpBgMEjnzp1JT09n5MiRpKWl4XK5OHjwIFu3biU6OpqqVaty\n8OBBfvnlF5o2bcqoUaMwm81UqlQJxaZgT7Jj89nY9EI5387x48dJT0+nb9++zJgxA4fXgepW0dI1\nRBOU4QoyQr+jN/bR+Z5kohCVGsW5c+fC57lw4QL1m9dHa6ch9wj2GnZGTxwN6Cue7du38+OPP1Jc\nXMxNN93E/Pnz/+Hc7tq1C1uCDTkjGIyGcM2hqKiI6tWq0dRo5KIIR0WoZrdXIOW7HjZjxgwqVarE\n5MmTufnmm7FaLGSkJ1G1ShWsVisul4tevXqFuY0iIiJ488032bx5M3WyKhEb6SYqYMdpFboZhbqq\nSvXMTDp37kxGRgZul4MZXRS2zRR6NzCQbolhukzHIAZmykyKpIiA2GmdqeM3WC3M7Sn4rEYMBsFm\nEV4pIwt8a7pgtxiuAFD+8ssvxAUCzFMU9ogwxGwmr6ymdLm99tprVEmPx2XVi/iDmuj4CpvJdE3Q\nFuiAylDIybp1wo8/6uR6cXE6Wrp5c8Ht1irQel+ypKQgn31Wfmc/bZoBp9PEwYP68+eeE6xWvW4C\nOi6iUiVh1iz9+c8/6+pzNptCvXqZTJ48jtOnT1O3bib9++udT2fPCi1b6hoVPXoIWVk6CM9sVrjh\nhlZER2s89ZS+qgiFNN54443/BYl/wZaLiOey514Refx6XOg32B885RXt4MGDREREcMcdd7Bq1Soy\nMjJYsGBBhX1++uknEhISGDJkCEVFRQSDQZ566ilKS0upU6cO3bt3Z/ny5Wiaxrp16/j+++8ZO3Ys\n8fHxjBw5khkzZqCqKg6HA5vNRk5ODhs3buS+++4jMjKS+vXrEx8fX6Gtdfv27cTFxdGuXTtKS0vp\n1KkTEydOvKoy3vHjx5k3bx4zZszg3Xff5fDhw+zcuTNMLW42CLayO3pB/2urZGPPnj0VznPu3Dnu\nu+8+Ro4fyRNPPMHu3bvLiOMiw+mlL774giZNmtCmTZtw2uJaVlJSQm5+LtaOVoyakREjRoRTM4mJ\niVRJTcVpNmM1mZgyYcLvTnvwt1ZcXMyQIUNQVRVFUahdq2Y4aLVr1w4RCa8MioqKyM7OZvHixYAO\nZIoKaLw+Vccq1EkSzIpgVpxYLDFkZdWgfqYzvAq4uFJwWxUGySAsYmGADKBIiqhkSmDhZdTie+8W\nIq02Is2RVK+WiVUVPDa9NuHQTKxZs4a3336bnTt3cvHiRV544QWau1zhFdhFETwWSxiECTpIK+i1\nsX6M8NoUvXMqzaTLpTpVwWMzXjN1tGHDBtq3d3LJ2ZeWCna7sHGjMHeu/v+rr75K584tCQYdZGUl\n8/bbb1O5chxvv10eJIYONVKlit6KOneujnVQFD3FdGmfbt0qssF26GAgN9fIc88JPXtayc2thc9n\n4J13yvd54gmdIDA/Xzh9Whg+XGXevHm0bduwQofXo48KN97Y4X9B4l+wD//JbX+k/cFTfqXt37+f\nIUOG0L17d1asWHGFs5o9ezZDhw6ltLSUW265JYxCjoiIIDk5mVtuuYWkpCRSU1PDx5SUlGA2m8PK\nc7feeiuxsbF06NCBYDDI6tWrAb2Au3TpUlwuV4UVzcMPP0xMTEw4rbNgwQJGjx591fdfUlLCsmXL\nyM/Pp1+/fuH8dGlpKS+88AKV4uMxegU5VxYkzgumCL176FIr3quvvhqm5zhw4ACx6bE4qzrRojWS\nqyTTsmVLVq9ejcVrQW4W1P4q/lj/P9T7Pnv2LIV3FFKzfk0cTgcNGjQgJSWF5s2bU1xczLFjx656\nZ3o97dChQwT9Ptq2bRsOCEOGDMFiNocFkSZOnEgwGGTbtm0A3FwwhN4N9E6ko4t1PIbTGlvmq5fQ\npElrqic7KFmpO/9fHxfMRsGmWrCYApgMBjyaEavJQFa8gb8uK9PybmkgyRIiOhhNx7Z5LBks/PCw\nvtJ44Ca9g6lStBDjE/xOlca1a5NktVJSFiROiGBX1QpdRBNvGcesbnqhfMsUoX+ukBahc0iVrBSG\nNBV6dG0X3n/btm00bVqLrKwkevXqSvXqDi5c0J3t4cN6a+nZs/rdfFqagtvtJCFBlzt99lmdkfX+\n++8jJsbGvHnCmDFGIiPduFwG8vKE8eN1LQqXS5gyRU8X7dypdzQtWKBf57vv9JTT7t3lK42oKAuR\nkUZuu60cR9Ghg1CpksLu3cK8eYLXa+WTTz6hXbtGrFlTEVfRp0/H/wWJf8E+EhHfZc99UpE2/D9h\nf/CU/3a7hC5dtmwZderU4fjx4xQXF9OlSxeGDh0K6Hfzdrs9XGg8dOgQFouF4uJiPvroI2JiYsJ3\nb/v27cPlcoWdMujpAafTSYsWLejRowdOp5P27dtz4cIFjhw5QvXq1cOB5ZIVFxdTUFCAxWLBbDZj\nMpkwGAy43W4OHz4c3u/06dMkZSag5CrII4LSVCGjVgYNGjTAFrThbOjE2cBJUtUkjh49SuO2jTHO\nNobbWG25Nlq1aoUW0pDHyuscxluNjBg3gn/Wdu3axcKFC3n66aevAMf9XlZaWsquXbvYsmXLVXP1\nBw4cwKeqOEVwu92MHTuWyZMnk5yUhEdRwrTpDoeDWbNmAfDjjz8S8jvIqyx0rqMD3pYOFpxaBiK/\noFmisZqNmIxCpWiFRwcLGVEKTquR+/oJs7vrOhIbxgi779AL2JrZgMemF66DXhdBlwmvXZjTQw8y\nTxQIVrOCYjBQK1mlbyMjHqcFr9mMwyCkKAoPiVDPZmPUkCEVxjht6m2Mam2gXopQO0mI8wv33li+\nevn8XiElIQLQwXGBgI6Sfu89oUULK2lpUTRpYue22wxERgqFhbrjLSxUiIgI0r9/fzp37ozbrVNl\n9OrlYMWKFbz22muMGVPA1KmTWbx4MTVqmNi9W08p1aihI6azsvQVhcul4nZbcTj0dldNMxAVpYY7\nrS5cEGJiNFwuK1FROulfWprgdhtp0yYPm81QJpNqZvbsQjZt2kRUlMaTTwrz5wsul5E2bXL/FyT+\nBbtJdGDbHSJyZ9n/N12PC/0G+4On/Lfb9u3biYiIoF27duH0A+jL+lq1anHu3DlmzZpFMBgkPj6e\nCRMmEBUVFe462rRpE/n5+RXOGRUVdQX45t5776VSpUoUFRXRuXNnoqKisFgsqKpKampqOGD0Htyb\nrMZZZNXLIjEtkeSayVTNqUp0dDQGgwGz2cy8efMqnLu4uJjxE8bjCDq45957KC4upveg3sjo8hSU\nOkpl6OihhJJDyBeXFb3nCiPHj6Ryg8rI1su2LxW6D+h+nWb9t9vFixfp2rENqTF2GldzExPpY9++\nfRX2ycvOZkJZR9V0RcFkNGJUFBwWE5PbCXWTBJ/LRIM6VVhdRrg4ZmQBY9uUdzjN7qG3qBqNVoxG\nExkxJv7ykPDXZULrLBNV0uPx2AysGVXumO/uJQxtpv//1EjBZfEi8jUWUxY5acKfi3QEtc2sEwK6\n7BZGjBjBzJkzyc1thNvlZOjQobRt2waHZiLSLdSvVYOlS5Zc0bL9zDPPYFWFXvX11crCvkJ+loRX\nOYsHG2jWqA4A06ZNIy7OgtmsEBensmyZYLOZycnJ4YYbbqBjx7ZkZ2ssXy54PGaGDx8eXn3l5jYi\nJ0dwOoVQyM7cubPDq/CuXbtisZiIj3djsZhYtKj8Ln/BAqFy5XgmTVL59Vfho4+ENm2sJCQEGTLE\nwvPPC927W4iIsBETYyImRu9+iorysmPHDuLiAmzcqJ/rxx+FuDhdQ/ull16ifftm+P0WRo0y8vjj\n1xcn8UfZtcYg/0KQMP0T+6wQkfdFJK/sAp1F5NPfeqH/v1lOTo4sXrxYRowYIXa7XYYNGyYGg0He\neecdURRFevToIefPn5eHH35YNm3aJEuWLJFWrVrJnDlzRESkWrVqsnv3btmzZ4/UqlVL1q9fL0aj\nUaKjo8PXePPNN6VobpGciTkjsx+aLYZigxz/y3EpLS0Vo9EoderUkXfffVcGjR4k3+V9JxcKL4hM\nFDFoBuEhRL4XMReYxWKxSElJiZSUlFQYg6qq0r1rd3nrjbdk0sRJIiLy7fffiowu28EgcqHZBdm/\nfL9kZmbKsXXHpGR6ichZEdsmm9QcUFN8Pp8cmH5Azqw4I3JKxHaPTbre2fUP+Qz+GVu+fLkc+Wqr\nfDLnjJhNIkvfNMjwwX3knR3lGdX9+/fLo6KLr9xRWio2EZltFnn1tlL561mRFdtFlgy8KGbTJzJm\n4jAx/n/svXd8FOX+PX62ze7OluxueiMFUgglhRRKIBCKECAJNSAg0oRQQhXpRESKItJEURBUEAQL\ngnRQFBG4SFMJCiq9BQgJpJCye35/zDIkUtQrer+f+7uH177YmXnmeWZmJ095l3NUKly5dA5pQRVy\nHfHVgdCQEGz74gCGZfZFU9N6eFulY1PSK9Bx/hWYFYBZf+/aXPRAmbOKM7mAhrcAnINCkYO1WYCv\nDagfAuz7CZi1AahdJxweHh4AgKSkpti79xv4+PjA19cXl86dQp/on/Hc2h/Qp29fKJX3pGRKS0sx\nNLMfYoOBVnUlTY5BzSXdjJrPAl5m4LtLGuzZtxzL316OV155BbGxsejZswkuXryIwYNXIyAgAK6u\nrjh+/DhatGiBJ554Cdu27YBK9TXKysqqtHXunKQZUVpahNTUKQCUaNnyCezatQtDhmTBbDbjp59+\nwoQJ6zBwYAUUCuD6dSVu3bqF9PRyiKKkK9G58x3s3NkSBoMPXn/9e5w/n4v4+J/xySeAUgk88wzw\n6acl8PHxQW7uTaSmStfg6QkkJSmQk5ODXr164fLly1Aq/4WFC0sBAH37PqaX678Ef2SQACSVuHxn\neQKoBuDc33VR/y1IT09HcnIymjVrhsaNG8NiseDQoUMwmUw4ceIEbty4Aa1Wi86dOyMyMhJZWVny\nuQEBAViyZAmaN28OtVoNvV6P9evXQ62WfrKcnBy0SW2D0s6lwPNAuakcqAkcOnQITZo0AQBYLBZ8\n9913yFXkonxuOaAAUARwHYHaAOxA2Q9lUM9WQyto0alTJ7z19lt4/9P3YTVbMe25aYiKikJeXh5a\ntWqFhg0boqygDIqFCrAFAQL6t/VoHN8YA54egMQnEpG/Oh8VeRVo3aI1nn76aQBAfkE+3qn/DtSC\nGpPGTEJG14w//Sz37duHF8eNQ+GtW0jv0QPDR4+GQqH4qz8Rfv3lZzQPkwYIAGhdl5i64UyVMqEh\nIdhy8CCGASgHsAUAFQoEuhOjVwEvdAE6xUtly+3FGNC/J0STB86YdWgdeQeCGnhlmx5PpKTDYrHA\nxy8QR77XOGsDDp0BNGV2TCkmRiwF1AOB4lJg/AdAck1g+HJgzZdAcLkKN3AYSoUdN4ukQQIAbhUC\ndgeQf+kS7HY7VCoVLl68CIPBAIVCAZIoKi6BhwtAEmVlZdDr741GFy5cgKAsR+d44J09QJcEQKcB\nQtyAc0eAwCvAUZ0SFy9exLNDn0V5WTmaN28BhUKB4OBgBAYGoVatWoiMjEStWrXw6quv4pVXriMz\ncyiWLl2KCRMmICEhAbdv38bRo0fx1luQhY6ys+0YPnwStFo9goODYTZLagRhYWFYu5bIzgaKixV4\n+20tmjZtiCVLtuPixQrYbMC6dTokJzfEs89KDggPDwETJwJ3tZm6dQN27LiDVatWwWIxYfXqfHTv\nDly9Cuze7QCwGYMGDUBFhR3p6X/9XfpvxR8ZJIZByovIBVB5qlnnb7mi/zKYzWbs3bsXO3fuRGlp\nKVasWIHS0lJERUVVmc3pdLr7lKw6duyI9u3b48aNG3B3d5eVyQ4ePIgmrZugdEApUAZJFuprAA2A\nUaNG4a233sKWLVtw9epV1KlTByyVOnQoIFEmvgEpZq0cQBDgqHBg686t+HD9h5i2fBqKs4uhOKfA\njuQdmD5pOq6WXMW50HPY+flOmM+ZkRSVhG88vgEItEhpgTHDx+CF2S/AP8gfCbYEjB81HtHR0XIn\nvuDlBVjw8oJ/+xl+//33SG3RAi8VF8MfwHMnT6K4qAgTpv71dJ2o6BhMW21AVusiWERg2ZcqREXW\nrVLmtXffRctGjbCqqAhXKipgDghA5wYxyFr5GWi/g5J7E2WczgU0Dg2UBcXIyS+D91AlAAWezGgP\nRzlh1utRXlEBs0mLczcFmHTE1mMONHYAfWEHbwLPzwd+AOBwaHHzSBkiHMR0AKNQBh1GIywiGikv\nH8O49g6cuABsOAa4AgjJz8e7ixfD4u6OnF9+gU6nw8GDB3Hu3Dmcv5iLBdsVaNq4QZUB4vLly7h2\n7RpuFlageS3gu3OA92DAQcAdgGAHQgCo7tzBjOdfQExJDHYrdyMvLw+urq6w2+3Iy7sBURQBSKtP\npVKJ8nJpAOzfvz/c3NywfPlykERERCBu3Tolt3/qFODn58CpU6dw9uxZFBYWwmg04pdffoFGo8GS\nJURgoANWK1BWZsfatRXYtQu4cwdQqdT4+OPhAIDy8nIUFFTgvfeAzp2llcTatYBaDUyfPhHu7kTf\nvsCECVrcuqVAo0ZJOHlyEy5cqEBeHlCvHjF/PhDzd7HRPUasWbMGzz//PM6fPw8vLy+sWLECiYmJ\n/9Fr+gXSO/j/Ev5B696/j+PHj7NOnTq02WyMjY3l0aNH+cMPPzA/P5+RkZFMT0/n1q1bOWTIEPr5\n+VVxSj8KzVKbEW9WsvNnSxnKgodAX19f1qlTh+np6Tx9+jTLy8sZ0ziG2m5aYhGoCFAQPpD0KO6A\nyACVRiVPnz5Nm6+NGA9iq+RvUI1UUWfWEQec7VwCtbFa1q9fnx4eHjQajfT29mb1utWp66wj1oNC\nf4E1Y2uytLRUvt78/Hz27duXERERfOKJJ3j8+PE/9RwnjR/P8QqFHMJ5FGCIl9efquO3yMnJ4dq1\na3nw4EGOGTmUZoNAPw+RdSOq8/z58/eVv3nzJnfs2MF9+/bRbrezqKiIs6gkDgAAIABJREFUA/r0\noJvVSKNOwbk9wZldJf/AjK5SGGliqIJtWjVjWVkZl775JqNEkRcA5gFsptMxrW1bLlu2jHv37qWb\nKPITgGcg8TwFAjQC9LfZqAUYDkn46B2AiZGRXLduHZOTGrBRgwTqdEbq0YRu0LM69FSrVKxbty7T\n0tIYHR1NDzcrh7QCRa1S1iMnyecnTqRFq2WEyURXo4GuLlom1xZo1kuhr54Abzuf+WGAWqWSUcoo\n1lPUo8GgY2xsPfr4eFKnExgdHU1PT08aDAYGBARUIb/7+uuv6eZmYP/+eiYm6qnXS8py/fpJnEt9\n+yqZnZ1Nf38v6nQa+vq6UhA0NBikxDm9Hpw0CdRqJUqONWvA3bslgr7evZ+U24mMrE5vb4muIyhI\nyqnQ6cDvvpN8Efv3S9s7duxgt25t+d5793wey5ZJ12K1/vs+CYfDwbVr13LSZIlt9lFUPf8utm/f\nzoCAAJmM8dKlS7ImTGU87B7wNzmuvwCg+Tsq/gt47A//caOgoIAWi4UvvfQSz549yxkzZtBgMDAk\nJIQWi4VGo5EDBgxgcnIye/ToQavVWiWj+VGIbhpNbKs0SCwHlRYlBw0bxEaNGlUpe+nSJWZlZVFj\n0VDpoqRCqyC6VDr3JxBuoGARpEzn/iBqghgCKkcqqVArJLqMZSCskAgG9aDZ3czoptHs0LUDFRaF\nRFvudGabapu4b98++RpatWrFvn378tixY1y0aBF9fHwemn1NSkl6lQfM7ClTOKIS5fY3AGv6+f3J\nX+Qelr31Jj1senZoYKafh8ipk8bx2rVr/PXXX2U9jYchNzeXe/bsqRJAcODAAfZ6shN1agXbR6pk\nx3P+W6CgVrKiooJPpqZyRaVs8S8Ahnt7c9WqVVJOjacnzQBdAfZwhqlOABjg7s6Zlc77LePsxYsX\nqdd7UJr7f0tgG/V6G/v27Ss7i9PS0pjRUMMQP6OcVf7ll18ySBS5B2CGRsNIrZZmnRQ51KKFFAFk\nFATGAgwFqFOrabVaqVFr6Kfyo14nZTuvWQNu3gwKgoZdu3ZlZmYmIyIiqsjYNmhQu0o+Qtu2Srq4\nKDlgADhzphQOu2jRIiYmGnn6NPjNN1Jk0pgxUs7FpUsSpYZKBT7//L16vv0WDAiwyu2cPHmSoaH+\nNBgUNBpBf38bIyLulSelwaNFi2SOGJHJoUNV8v7Zs6XMbKXy4YPE78mXPpP1DA2RBmIKaKhvYMee\nj1++tEGDBnz77bd/t9zD7gF/0yDxNiRjxnhIvE3/v+du+iOYO3cug4ODq+zz9/fnt99+y5UrV9Lf\n37/KsYSEhCoqVo/CS3NforqumjguZUNrAjQcljWM9erV46xZs+RyZ8+epdXHSriBWOrsxHNAmEEc\ncW6/DyIehAESNQchkQJ6gdBJXEzo4hwgTjqPH4Ckfb0OFNoIVFh/M0jUMfGbb74hKc3ADQZDlfDV\ntm3b8qOPPrrvvhwOB0ePG02VVkWloGTiE4m8efMmf/31V3qYzcxWKrkMYJAocsqkSVyyZAk//fTT\nPzVjKygooNmo5U9zpI782hugl6v+Pu7/yrDb7fz88885duxYmT3XbDbfl0DZLqUdm9e6F52U+zqo\n06ppt9s5IjOTI9VqubOfC9AAHyoUsfRw9WYfQaA3JH6nu2U+Bli7WjWGGwy8BLDcmS3ds5JQTVlZ\nGY1GNwJfEsilKLSgXqthXFwcp06dysmTJ7NWWBB7Jiro7WGRB9/XXnuN3bVa2tRqNklMZHJyMl1c\nXFirlpLVq3uxQ4cOdHV1pVqlokZ9T8s7MzOTarWagqBhjx4KkuDLL4P169eTB6XRo0fT7NTNIMka\nNTyZk3Ovo549G2zWrBGTk+uxQ4eWPHToECdOnMjQUIHPPy/Jk9psUhTS3XPGjQMFARw16t6+XbvA\n8HDv+96h3NxclpaW8ty5cxRF8ORJqfyxY1JiX3x8JK9evcpq1dyYlAR27Aj6+EjCRGaz4oEd7I0b\nN+gX6kd9Jz2FwQJFt/vlS7VWLZHv/DsoBsVq98uXDh87nCYPE62+Vs5+5c/Jl1ZUVFAQBM6aNYs1\natSgn58fhw4d+kDK8of1kfibBolsVOVvuvv9P4k/9XD/KZSWljI7O5tPPPEEo6OjabPZ5KSvwsJC\nms1mfvfdd5w3bx6tVitjYmK4fv1653LcrUoG7KNgt9v53KTnJJU6IwhfEC5gfOP4Kh3mwKyBVI5U\nSgMAQVwGUV/q/KEBEeXkSVoHwq/S6oKgop6Cb7zxBq9fv06bj00qW/mft3N1USoNOqoOKmIDKAwU\nGF4vXDY3FRcXU6vVyisHh8PB+Pj4+yRN79y5w6hGUYQJhAeIFqDmaQ3Te6STlGaJmX36sGd6Okdk\nZdFDr2dfUWS00chObdr84YHi1KlTDPQ2VOE7ahbpwu3btz+wfEVFBdPS0ujr60uNRsN+/foxOzub\nI0eOpMVikWeUby15gyaDQFEAh7eWQlbjQ0WOGSUlM16+fJlBnp7sYDCwA0AdDASOE7BTCxvnAxwB\nsB7A65CEbWLVar4yezanT51KnVpNrUrFJxITOWroUPparQx0d+fCefO4bds2iqIrTXodh7QEv5oM\n+rhqaDIZqdfrqRU0dLcauH//fvm+du7cSVeNhtUDA6nT6ejj40OdTkeNRsVu3bpRFEVmZGSwZ8+e\ndHV1lQeA7Oxs+vj4sGPHjjSbTezTR+pca9YMlY8PHDiQnp6ecludO7dnQoKGu3aBP/4IBgWJTv6w\ndKalNafNZqUgCExKSmK9ejF0cVHTbAbfeONe/kNCAtirl2R6mjhRWmWYTKC/v9cjZ+vVqrnRYADr\n1ZMS80QRnDRJCjPPz8/nk09mUKdT0d9fZGCgBz/99NMHdrBTn59KTT/Nvff/A7BuYlX5UmN1Y5W/\nEZeEqvKl2S9mU2wkEqdBHJcYDN5b9cflSy9evEiFQsG4uDheuXKF169fZ6NGjThx4sT7yj6sj8T/\nuJv+s3jqqafYunVrjhgxgqIo0mq1snbt2pw2bRojIyPp7+/P119/nWFhYdy5cyfXr19Pi8VCnU73\np3WgHQ6H5EPY6nwl80BDsEFmIiXJDr06EG85VwEHQKSAGCvN9nHeuVro6Nwvgngd0opgI6iz6pje\nPZ3VY6qzVlwt6fjdPIj9kAanl5yDhAh2f7o7G7RuwAHDBjAvL4+kNJgNGTWESq2SCo2CjZIbsVOn\nTqxRowb7DOrDGbNmyIPo5GmTqX5CLdVXDqIHiL6gW6DbffdtEUUec862ywBGGY0PJZH7Le7cuUMf\nTys/GiENEPuyQTer+EC7LkmuXbuWQUFBHDp0KM1mc5XOMiIigp999hm/+uorertqmfMSeGY+GBkA\nWg3g6JEjaLfb+emnn7J541gmxtdm3z5PE1AQ+Jn3Fg1JDFSp+DPAfgDVADUARw4eTLvdzoKCAqam\nplIQBBoMBlYXBJ4EeASgv07HAf37c+HChfSyaWVivrJ3wLoekt7FCoAt4uLuu7f6sbEUBEGmPrm7\nSqhXrx5r1arF7Oxsjh07ljqdTs51GDx4MPV6PUePHs22bdvSW62mAaBWKzAysi5btmxJg8Egr2hf\nf/11WiwW1qwpUaobDFqmprajzabjwoVgQoKGZrOZ3bt3l59rXFwcq1dXUK8HExLUDAlR091dycRE\nLePiwCZNFDSZ9KxXrzZdXMxVBLF+i0uXLtHLy0KbTUmbTclmzRKq8I+RUlLrqVOnZGrzB/UvmcMz\niTmVhoBjoG/NqvKl/uH+VM5UEhdAxesKuvpXlS+tk1iH+LxSHW+DHXr9cfnSvLw8KhQKvvvuu/K+\njz76iNHR0feVfVgficecJzEfwHAAGx/USQNI/bON/TejpKQEH3zwAb799lskJyfj8OHDCAkJwbhx\n4/DCCy/Az88Pt2/fRnZ2Nt577z00b94cADB9+nQsWbIErVu3/lPt3blzB/m5+UAr5w4rgETg1KlT\naNxYkiTvktIF26ZuQ3F2MZACoATAckhRTn6QyODXACgG8AqkqKchAERAoVZgs8tmlL1eBnwEKdwm\nDkAwgPOQoqRyAbQHEhsnYtXbq+4LSZ0zbw6W718OxzkHoAAOtD0Anws+uG66juXhy6Hbo8PaVmtx\n4PMD2Hd0Hyr6Vkj1AkBvAKMAbx/vKnWWlpai6M4d1HZuawCEOxx4++23cfLkSfTu3RtWq/Whz02r\n1WL9xm3omNYG/d8uhEKpxrsrP6iSf1IZ58+fh7e3NywWCyoqKnD69GkEBQXhxo0buHjxIsLCwvDB\nBx+gR/1S1PSVztn+HBDyLHD56q/YtWsXBvXrjtd6FkMvAMNW/QqlUguH41UAL0GSiz+MsKQkRO/d\nizISCocDCqUSamcI64ABA3DhwgWMGjUK+fn5WL1iBc4COKcAShR3UHxiOV7brkVhcTmKSgGjTsp1\nKJWionEWgFpzv1tx7IQJ6N+/v5xb4enpCbPZhKNHj8Dd3QMOhwOiKCI5ORlLly6Fi4sLCgoK0KRJ\nE5hMJuReugQfEoIgAF5l6NDhO+TlKeHq6sCRI1/j1q1MjB49Gv3794fNZkNxcTEWLFiAY8e2YPx4\nO4YOBWbNkiKiTCaTfF0uLi6oX1+J2rXtmDTJjjZtuqFHj6exY8cObN/+Gn74oQwDBw6ExWJBaWkp\n3nzzTRw9ehRRUVEAAIfDgbNnz0Kn08Hb2xtnz17FDz/8AEEQEBERUSWqEACsVusj3xkAaNuyLd4Z\n+g6KnygGPAH9JD3atWonHxcEAV9t+QrdB3RHzrwcBIUEYfW21VXuy9XqCpwC0EzaVp1Swd3i/sh2\nf3udfn5+f7j8P4F6zv+TcE+69O4n6T9xQZXwh0fffwp3zSrr1q27L1Pax8eHZ86c4fnz5xkREcF1\n69bJx1544QWmpqZy48aN95Hn3cX169f5xRdf3BcV5FPD557+wwVQ9BPlqIe7GDRkkDTrdwPhCeJD\nZ/lypy9ioXMVcfefHRS7ilQYFYTdue8HpwnIC0SIc2XiB6IBqPHSyHTRv0Xjdo2JTyrV/QGoEBTE\njXttGWONXLx4MZu2akp1L7W0ynGAGCKtVtK7pd9nToivVYvTnGyw2wEaAMYCTARo02qrRPA8DHa7\nnVevXv1dqo89e/bQzc2Nw4cPZ69evajVamm1Wmk0GmVq97feeotJEUp5Fr/1ObCaD9ilS2v26t6R\nb/S9Z9r6bAxYO9SPSqWZgIqASE9PLxYWFrJ79+6Mi4vjlClT+Oyzz9LPz4+rVq2iq6srR44cKc+0\nExMTqVaAOhVk30rJctDDRcX4UB3n9QKbRYB1BPBNgN6iyI0bN953b7m5udRo1PIqQaJrUbNmzZrU\n6/U0GAz08vKi2Wxm06ZNabPZGBMTQ6vVyrDQUGo0GtaPjWX79u3p5mbh888rSUqaDX6+Jp46dYoe\nHh5VVl+hoV5MSbnHvxQXJzA8PIzVqlXj4MGD+dRTT9Fk0vLzz6WopOrVwdhYSfWvrKyM8fG1aTTq\nq9RZrdo9wa7r168zJiaGNpuNRqORPXv2fGgwwsNEkR7WvyxcvJAWbwv1Lno+2e/fky81uBmoGaKh\nto+WNt8/J19KStr0cXFxzM3NZV5eHhMTEzllypQ/fA/4G8xNagDvP+5KHwP+1IP9p9C3b1/Wr1+f\nbm5uMg/SgQMHaDabZY7/zZs3093dnfPmzeO0adPo4uJCi8XC1q1b09/fn6NHj2ZZWRlHjBhBX19f\n+vn50Wq1slGjRvT29ubIkSPlTvPw4cN09XOlKcRErVnLWa/Muu+aNmzYQNRxdsobQNhAJIOacA3h\nCkm1zhfEZ/f8FnpfPQUXgSh27rsKSbzI6SjHt05fxheg1kX7wEiloqIiNm/fnMqpSnmQUI5TUilW\n0p8gqIvSUXAVqO2rpcJNQQSDqAXpmn8GDbEGvvPOO1XqPnv2LBvUqUOVQkERYH+ADqftJhtgUmzs\nY/1d58+fT51OR1EUWbNmTW7durXKPZeUlLB2eBBjgsBeSaDVBLq6arlx40b2692dcypxIK0ZCvq4\n6WkyCAyu5sEJEybw9u3bJMnAwEDZ9JOdnc1WrVpx8ODBDA0NZY8ePWQt7zoRNSQaDr0kVHS37vT6\nRg4cOJBDBvVj1rBh7J6Wxu7t28sd6IPQs+eTVKvVtFgsVKvVVKlUdHFxYb169divXz82atSIFouF\nEydOZEhICJs0aUJBEGiz2Rgaes8PMXz4cBqNKpaWgp3SVNTrFCwqKqKnpyc7d+4sS8paLGp++ino\n7i6xs778MqhWq+jmZqNOp6NOp+XQoVLUU2KixN8UFRUqO9w3bNhArVbDdu3accqUKezZsyc1Gg2H\nDh3KhQsXMjk5mQ0aNODUqVM5YcIE1qhRg6+99lqVe87JyWGdOsFUKhX083Plrl27qhz/O/uXvyJf\nSkqRf4MHD6bFYqGXlxeHDx9eJdz8Lh52D/ibfBJfA9A+5jq7ADgOKTnvt+kr4yEtyn7EPWPKb/Gn\nH+4/gfLycs6cOZN16tShi4sLY2NjKYoiGzRoQD8/P06cOJFr165lamoqw8LCGBMTQ7PZLIeL5ufn\ns1q1ajQaJebTX375hf7+/rLsZ35+PsPCwmRxFFLqoI4fP/7QkNJr165RY9YQs5yriQYgTGDdenUJ\nCyRFuJWQvgeDWquW02ZNY8ceHSkmi1I+RltIoa8ukBzWFlDhpaDoJXLeonn3tXn+/Hn6+/vTaDQS\nIqhsr6SYIdLqY2VUYhSF/gJxDFQsVEiDz7eQI0JQHRI3VIlz38t4KCFgWVkZrUol36sUEfQ5wLBK\nTtPHhdLSUt64ceOhTtLS0lIOH57FsLBqbNCgthy9dejQIbpZRc7uJjG0mvWSY/v6G+CC3goGVfOU\nZ6SJiYls166dTI1ep04dzp49m9u2baNGo2FUVBQjQqqxboCGhcvA1Hpg94YSxfieKZJv5UHqf4/C\n0qVL6eXlxaeeeopjxoxhjRo1aDQaZfrzqVOn0tPTk02aNKFGo6FGo2GtWrVYrVo12W9x13ehVCpp\n0CkZKvpRq9HS4XDw22+/pY+PD/V6PTUatczO+sYboM2mZPPmsRw8OJMNG8ZRr1ewTh0FjUYjo6Ki\nqNVqZb2V0NBQ5ubmcsuWLfT01NBkMlGhUNBsNjM5OZlms46DBuloMOjkSKzs7GympKSwb9++8v2W\nl5ezenVvvvGGgna7pGrn5mbgpUuX5DL/r/YvfwYPuwf8TYPEewAOApiMxxcCGw4gFFIORuVBIgIS\nDbkGQCCAnyFR5vwW//Aj//M4dOgQDQaDbP45fvw4jUYjk5KS2Lx5c5rNZoaFhVGtVlc5r23btvT0\n9OSRI0dYUVFBpVJZZbk8YMAALl68+IFtFhcXc9myZZwzZw6PHDki79+9e7cU4bTD2fHeBnVBOgqe\nAtEGRCiIlqBKq5LJ7crLyzl33lymdkuVZv/ZTlPT1yD2gEKgwJmzZvLmzZty0t5dpKSkUKVS3X0h\nqdFomJ6ezqtXr/LmzZvs+nRX+kX4sUGrBlSoFJKz+m5UVS8F0fmeSUzfWs8FCxY89DnX8PVlAsAC\ngHcAtgHYqW3bh5b/J7F161aOHzuWY8aMYb+nn2Ra25YM9ROrRFbVDDDx2LFjJMnvv/+e7u7urFOn\nDoOCghgXFyc79sOrVaNeCU5KlyjFby8DgzwFhlb3p0qloJe7hZs2beI333zD1q1bMzExka+//vrv\nxun37t2bbdu2lTvVbt26UavVcuLEifJgZTKZ6OHhwdDQUMbHxzM7O5sjRoygTqdj27Zt2bdvXwYH\nB1On0zEKUfTSe3HGCzPkNhwOB/Py8vjVV1/Rx8dGi0WgxSJy/fpP5DK7d+9mZKSRarWKY8aMYVxc\nHOPj4zl16lROnTqVDRo0YKdOnTh8+HDq9VomJibKglRSoqaWJNiihYZNmzaVr7127dqcPfteqOmZ\nM2fo6yuycu5Ey5Yu3Lx5s1zm/0L/8nt42D3gHwqBvft5HPjtIDEewHOVtrcCqP+A8/7hR/7ncfz4\ncYaGhsrbI0eO5MCBA+Xt6dOny6apZs2akZTCPG02GyMjI/nxxx+TJGvVqsVly5aRvBup4cWoqChO\nnTq1ik21qKiIYTFh1LfSUxgmUO8hiRmR0oxboazkYyCo76NnQtMEGmsbqRukoxggcsbL9/6wSemP\nO7VLKpUeSskP8VEl/8JqMCg6iIJRoOgn0j/Mnz///DNJMjg4WB4g7n66d+/+wOcUnxxP9bNqKft7\nP6hz09HiaaG5iZnGcCMbt278wOX0Xfz444901+upcUYE1Q4I+MOZ638nFr76KgNFkdMAdtDpGBcR\nwWPHjtHbTc/by6QB4uaboKuLroq+Rm5uLj/88ENu3ryZpaWlvHbtGocPf5bNmrWlSRBo1IJJ4aC3\nVcXBA/vQ4XCwrKyMJ0+e5MyZM2kwGJiamsonn3ySPj4+VXI58vPzmZ2dzWeeeYbLli3jRx99xIyM\nDPl9umviuiunm5qayho1ajA4OJhTpkxhTExMlQGlQ4cONBqlMNvGjRszMjKSTZOayu9uZbz66qvU\n6/W0Wq0MCAi4Ly/lwoULFASBer3kbwgMDKS3tzf1ej29vb3ZqlUriqLIRo0aMTAwkCqVihEREXzq\nqafo4eHGWbOknI0zZ0CdTmBQUBB9fHyYlJRUJZrp1q1bNBoFnjkjDRC3b4PVqok8dOiQXOb/Qv/y\ne3jYPeBvGCSiIZmGaj7uip347SCxEECPSttLATyIMvQffuR/HsXFxfTy8uL69etJSlnH7713LyZ6\n165dbNKkCffs2UMXFxe6ubnRYDAwIiKC27Zto5ubG8eOHcuUlBQajUZ5ptauXTvu3LmTLVu2lHUp\nDh48yMAagUQz3FOS+1Jy/GY8ncGysjIG1wmmYolCOnYGFH0lJ/emTZu4cOFC7tmz575ZZ8/+PSVz\n0DUQaZBCZO/+exVUeaiIK05/wytK1m0oxY136dKFGo1GHiBEUeSrr75Ku91+Xz7DlStXWL9FfSrV\nSlq8Lfzwow+Zn5/P7du3c+/evfIqKj8/nykpKbI9/P3335frKC0t5eHDh3nq1KnH/0P+G3A4HDTp\ndDzlNIE5ADYzGrl69WoO7P8Uo6obOLa9irUDDRyZlfnQem7dukV//zBqNEMIvE29PpJt2rTn3Llz\nuX//fm7evJktW7ZhWFhdqtUWKhTVmJSUJHfi/fr1kycqRUVFrFmzJmNiYuQwVbPZTG9vb+p0Onp5\nebJ27QC6uqrZtKmaoih1tEqlUs4NSU1NpclkYmZmJkeOHMnAwEBGRUXRZrPx2WefpYeHxwNlTvft\n20dXV1eOGDGC2dnZbN26NV1dXXno0CF27tyZDRo0YEJCAv39/Wmz2diyZUuazWY2adKEY8aMYceO\nHSkIAn18fCgIgmxiCgoKosFgoE6n4bRp4JEjYFoa6OIicuXKldy3bx+vXbvGjIwMBgUFsVmzZjxx\n4gQXLJhLX1+RffroGRKiY5s2zeSwbfJ/g8Rv8agQ2CkAekKiCX8JwEwAb/6JuncA8HrA/gl4cFjt\nw/DAm8rOzpa/N23aFE2bNv0TVf790Ov1+OSTT9C5c2f07t0bDocDBQUFaNeuHbRaLV599VU0atQI\nLi4u0Ol0yMzMRFZWFtLS0jBx4kTUrl0bixYtQkZGBk6cOIElS5Zg3759WLduHXQ6HerVqwcvLy90\n6tQJnTt3RrFQDKRBCm8FJIOeBthwaQOmzZyGFye8iEGjB+HWuFvgbYJm4vLly2jYsCG+++479BjY\nAxdOXoDR1YhZU2chJSUFaz9cC/gAcAMwEVIY7UUABIRFAhxtHYCn1JxjgAM5E3IwYfIEWN2s8PLy\nwvXr10ESLVu2RM4vORBEASTRolULDOo7CMnJyfD09MS+HftAskoIbcuWLeXvP/30Ezp06IBTp06h\noqICeXl56N+/P4KDg5GQkABBEBAdHf1P/Kx/CA6HA3fKy3E3WFEBoJrDgcLCQrz+5gp8/HEqfvrp\nJ0zvVwupqfciyd9dsQIrFi6EWq1G1uTJKCwsxM2bQSgvXwTgAFQlOnyz5TMkJtTDhx9+iDfeeAN+\nfoE4d+4XOByZANSoqNgn13eXERYANm7cCJJo1aoV3nnnHbi6ukKlUuHChQvw8/PDxYsXUb16OcaM\nIV57TQOlUoX69eujrOwMVq58FyaTC+7cuQNvb2+sXLkSxcXFMJlMuHDhAtRqNebMmQO1Wo2ff/4Z\nSUlVgx8PHz6MGjVqwGKRVJDj4uKwdetW1K9fH66urigoKIDFYoHD4UCrVq3w8ccfQ6FQoFmzZlAo\nFKhbty4OHDiAK1euAJBIA81mM6xWKz777DP4+wfgpZeuYPr0MsTExCAszIGRI0di//79SEtLg91u\nR+vWrXHmzBnEx8ejY8eOGDJkEhYtmgubrQjkQURFhWL37n8hKCjo73ot/iPYvXs3du/e/bfVnwNA\ndH53BfDt39DGb1cS45yfu9gKIOEB5/2DY/Jfg91u57Vr11hWVsasrCwKgkCVSsUmTZpwx44djIuL\nY2BgID/44AOWlZUxNTWVvr6+DA8PZ/XqEtncihUraDQa6ePjQy8vL+7du5dXrlyhXq9naGgoMzIy\niKedIar7QeSB6A6iqxTR5F7dnYZggxQ9ZHQ6h11AmEG1TS2dN8HpLP4ShAHs/GRnGmsapWijhSCm\nQsrUNoIGVwOfe+456iP0UlKeA1L7ehDtpTIIBBWiguEx4UzvlC7t83SWqQ5qm2jpEejxuyGAe/fu\npcHNQDwDooPzfIAqlYozZsx45Ln/DhwOB99ftYp9MzI4duRIXr16tcrxO3fucPny5Xz55ZdlmdkH\nIa1FCz6l0fBXgB8CdDMYZInYB+HdFSsYLIr8zFneS6/n2LFjaTB0JpBDPQx8GxLnU5ROR71ez2ef\nfZbZ2dkcNWoU1WoNlWhNtdqFzZu3YHp6Oq1WK5cuXcpdu3bRzc0bCmCEAAAgAElEQVSNoaGhbNq0\nKevWrSubl9q0acMaNWpw1KhRVKlUFDQapqSkyKu2kJAafOKJJ2g0GuVwy4YNG1IQBPmTkJDASZMm\nccCAART1etkfVlFRwaNHj8rO75iYGPkctVrNp59+WqbxMJlMsunqbpTVmDFjmJ2dzUmTJtFqtVIU\nRarVak6ZMoWTJ0+mVquVw3fHjx9PFxcXmbMqKSmJvXv3psFgqKJBHhAQwOjoaJrNZiYlqWRVuxkz\nlOzaNYXk/1YSv8WjVhKlkNKsAOAGHuxAfhyonIG1AVLI7VwAvpBYiv/1N7X7j0CpVMLNzQ0AMH/+\nfLz88suYPXs25s2bh379+kGpVKJmzZro2LEjFi9ejJKSEvz6668QBAHZ2dno378/Dh8+jIMHDyI8\nPBybNm1CamoqAgICMGzYMLz55psIDw+HYbsBRS8VScbB65CS63IA5VAl8iryYN9nlxLhPgbwDCQv\nU0ugYmEFsArANAAqAE0ApAKfbv4UJoMJ6ANgAYBLAIYCGAAUzSzCSwtekqYQIYDSRQmH3SEN7/MB\nfAOgDsBDxI/JP+LHnB+lhLwUSCEQKUDpnlLceOsGssZnYf2q9Q99flkTs1A0r+ieEXIQgKVSUpyr\n6+MnJ37pxRfxzsyZGF5cjB80GjRcswYHjx+H1WpFaWkpmtevD/2pU6hVXo52KhUWLF+Orhn362N0\n6NkTwz7/HB8DUCoUqBcf/8hZ6oqFCzG/uBhtndvXSkrwxfHj0GgOAxiKJiiGGkAtAMPv3MFYNzcY\nDAYAEh29i2iG9dY5nKnwwZdf7oNGI2DYsAFITk5GdHQ0kpOTsW3bNpSVlSEiIkJetfn7++Pw4cMw\nm83QarVo3rw56tWTUqTUajVOnTqFBg0aIDg4GMuWLcOpU6dQVFSEGjVqoGPHjpgxYwZatGgBtVoN\nX19fhIaF4euvv4ZGo0Hz5s2Rn5+P8vJyqFQqCIKArKws5Ofn45133kFgYCAAwGQywdvbG2fOnIFW\nq8WQIUNw8OBBLFu2DBERETh79izKy8tRUlICg8GAjRs3Ii4uDiTh5SUZK7RaLby8vHDr1i35mRQW\nFsJut6O0tBR6vR4OhwOlpaWoU6cOoqOjsWnTSigUkvpBo0YOfPbZ2b/07vy34lEdfzAks9DdT+Xt\nDX+x3Q6Q8nbrA9gESccFkFYva53/bwEwGP9lXCO3b99GVFQU5syZgwkTJmDevHnYuHEj1Go1fvrp\nJ7Rr107KYIWkJ3Hy5EnExcUhPDwcANC2bVuQROfOnTF9+nSUlJRg+/btiLPGQTdZJ635FIAYIMLY\n3QjjViO0DbRAAQALpKcZCmAEpB5nsfPC9jr/rwBwCLALdowfMR6hn4UCVwB4QOr8owFsBJhMUEHg\nNcChcEhvhwJADdxTGqkHwB+AC6QBApCytmsByAHsiXacPn/6kc8r72YeEFZpRy1ApVMhICAAPXv2\nfOh5ZWVleG74cEQFByM5Nhb79+9/ZDt3MXvmTHxWXIyBABaWlyOyoAAfffQRAGDt2rXQnjqF7UVF\nmFdWhg0lJRiVmXlfHQ6HA4MHD8ZthwOFAG6R2L9/Pz7//POH32deHooqbRcBUCiV2LDhA5hUX8IO\nYj2kZffrAG7n5+PUqVMgiZycHJQWl6IL0gD8DJKIjY1CUlIS9uzZg+rVqyMyMhK9evVCYWEhDhw4\ngKKiIlRUVGDv3r3w9/dHTk4OHHa7LGoFSIOENPkERFF0akfkwWazoV69elCr1RBFEVevXpXv+8qV\nK3B3d0dqairi4uIQ7FQXstvtUCgUEAQBPj4+UKlU+Oijj7BkyRIsXboUp0+fRkVFBWrVqgWj0Yhm\nzZqhefPm+Oabb3Dp0iWEh4cjMTERZWVluH79OtasWQNAEtkCgCtXruD06dPQarW4ePEi/vWvfyEj\nIwMDBgzAmjVrsG/fPqxevRparRYBAQEwGAy4fRsoKABKS4EpUwQ4HDosWPDv6578t+JRK4m032y/\nUun7X+24P3F+HoQZzs9/HU6cOIEWLVqgdu3auHbtGsxmM7Zu3SrbjWvVqoV169ZhwIAB0Ol0WLVq\nFUJCQnDkyBFcuXIFXl5eOHjwIO7cuYMdO3bAZrOhbdu2+Pnnn3Hp0iVYy6woOFmAXbt24cKFCyAJ\nv8l+aJrSVPItFEAamq9DylBRAbgFwAGgM4B2AL6HJERkc2D8zPGouF0BNADwubP8UkjUHp8BOAKJ\nYqCrs553IK05f4TkE/kO0gqkBJIyehikAecEAC9AN0WHpPqPTt5Pa52GJROXoGR5CXAD0LyswcA+\nA/HSSy9VEc/5LYY/8wxOr12LpSUl+PH0abRv0QLfHDmCkJCQR7ZXWl4Ol0rbFodDlt/My8tDzYoK\neelbE8CN27fvq6O4uBh37typsq+kpAQHDx6U6Vh+i/O3bmEApJ+mGNJCL5lEt65dIajVCAEw127H\ny5AchBsqKpCxZg1uORzQqlRIqGgMgoDCAVeNAzUOHcC4jAy4162LvDxJ0tbb2xvdu3fH64sXY96c\nOaBCAa1ajZLycuTk5KBRaSm2btkCjZPCY8uWLYiKisKlS5ewfds2mBUKlKhUsNls+OWXX1C9enW0\nadMGK1euRHh4OHJzc3Hz5k34+vriwoUL0Ov1KCgowHPPPQelUonVq1dj0aJFGDBgAMxmM65fv46U\nlBQUFBTg008/hY+PD06ePAlPT0/cuXMHxcXFEAQBsbGxso/Kw8MDhw8fxqhRo5CTk4NPPvkEO3bs\nkAW7Nm/eDKPRiMmTJ6NLly7o3LkzYmNj8dlnn+H8+fNISUnBlStX8MUXXyAsLAyenjkgHTCZKpCZ\neRhffXX8ke/H//B/B/+Ybe9xomXLlly0aBFJyVeRlpbGOXPmyMcrKir45JNP0tXVlV5eXrRYLPTw\n8GBmZiY9PDyYmJhIURQ5bNgwbtq0ibVq1eLs2bM5aNAgRkREMDk5uUooH0nOmTeHgo9A9HPmOdyl\n6GgJYi4kynERhBJEPUiMsBXOxDY1CC2I6ZWimn4BUa3SthFEEIh0ENMgZWLrnbkXZhC9JR8HbCCS\nICXtaUGlTsk2ndr8brhqaWkp+w3pR4OrgVZfK+cvmv+HnrVFr+flSkl2mYJwH7X3gzCgZ0+21uv5\nDSRKC3ejUdb5OHr0KN31en4N8CbAgYLA1ObNH1iPVqu9Lww4LS3toe16eXlJkWAAdZWiwjp37szM\nzEzWCg5mL7WaWwG6AGyjVtPbZGJNX19q1WqaoKar0kCdUskTAOcArA5QD1Cr1dLHx4f169en1Wql\nTq3mCYBFkESFAkSR3Tp25IsAY1QqutlsDA4OZsOGDSmKIn18fKhVq5kF0KjRMMbJcOzl5UWbzUa1\nWk1RFKlUKtkAYJozD0ir1bJTp06yP6BXr1602Wy0Wi0UBIFDhw6t4ivQaDRUq9VUq9V0dXWVI7BS\nUlLkcn369KGvry+nTp1Kb29vhoaGsnv37oyPj6efnx9HjBjBhIQEdu3a9T5/165duxgXF8ewsDCO\nGzdO1i3x9rbw8GHJN+Fw4H8+id/g7/Iz/A8PwNmzZ9GsmcTupVQq0aRJE5w7d08qXKVSYcKECQCA\n5cuXIzc3F2+++SY2bNiAL7/8Et7e3sjMzMSCBQuQkpKCpUuXYtWqVXj99ddx/Phx7Nq1CzGV9BeL\ni4sxYeIElO0rk1YA3wEamwYp8SkYEjYE/U71g3hJlExAfpAilzwhrRh+BWCAtCJYCuAapBXHXNwL\niH4LkonpKoCdkExXwZCMkUZIpic7pOgoB4AoAO8C6AiYXc2IDIvEjh07AADvvfcegoKC4OPjg4kT\nJ8ozQ0EQsHTRUhReL0TehTxkDbmnA/4o6AQBeZW281Qq6HS63z1v0bJliBo0CMNDQ/FJYiK2ffUV\nAgICAACRkZF48/330cPdHf5aLa4mJWH52rXyuV9//TU6dOiA1NRUeHp63lf3b4nlKmPQoEEQRRHF\nAO5AIr2rXbs2ateuDU9PT6R06oSPSLyi18MrKAhHXV3Rb/hwZAwYgLapqXAXlIhTlkJJYg+AFZBc\nSWqNBr169UJ8fDyKiyUX4ytz5yJZFDFEFBFvMKB5ejpqRkZipkaDNnY7vG7fxpnTp3HgwAHUi4xE\n3ZAQhFRUwA2AubwcZ374AcLt28i7cQMFBQVo1aoVunbtimeeeQZH1WpcvnQJS5Ysgd1ux9mz9+z8\n586dg7u7O4qKiqHXS6srAPjxxx+Rm5uLwMBAjBs3DuPGjYObmxvq1q2L4uJifPHFFzh79iyuXr2K\nzZs34/bt21i8eDGuX7+Orl27IiwsDG3atEF+fj42b96MsLAwXL9+HQ0bNkR+fr7cvr+/PwICAuDp\n6QmLxQKFQgG9Xo/bt0vgdI/gMcim/20wGo0wmUzyR61WIyvrj/09/BX8EY3r/+ExIT4+HosWLcKi\nRYuQn5+PlStXYtSoqsnrx48fR1JSkswKm5aWhj59+mDLli0oLCyU/RWAxAR711T1IOTn50MpKoFq\nzh06QFFdAaVSiXVr12Hx4sV455N3ABOkjv1HSL6DLgDWQxoszkHqtQIhkbN4QxK0NUKakyicx1WQ\n6EZtuDd3fg5ABoDukMxR85zX0RrIF/MxSzELhrEGpH2chvXr1sud2Lx58yAIAqY+QMP6+PHj2L17\nN6xWKzp16gSt9sGMMROys5E6cSKGFxfjR40GB11csDgjA0eOHMGnGz6FQTTg6aefhrt7VRZOQRAw\nc+5cYO5cAFLHtm/fPoSFhcFmsyE9PR3p6en3tffVV1+hdevWKCkpgQqAQaGAVqFAGQklALVWi6ee\neuqhv9WUKVNgNBqxatUqmM1mJCQkYOvWrfLxoqIilCkUqNahAxqGhGDXrl2y/yA4OBg77XZ8b7fD\nA8BrkOIQHACqeXrCz88Pfn5+iIqKwiuvvILmrVohoWFDHDlyBJ28vDB16lTsOXgQVi8vzL58Ge5m\nM0y3buHZ8nLc3LcPb0GyGhogUS34lJejCEC5QgGtVosjR44AAEjC5OKC2nFxyMjIwMSJE3H8+HFc\nvnwZOp0ON27cQN26dXH16lVYrflYvXo1mjRpgmPHjsFsNst+DgCIjo7GoUOHoNPpUFhYiPfffx9K\npRJWqxWFhYUoKyurEjJdVlaG4uJidOnSBRqNBsHBwTh9+jTWrl2LLl26oH///tiyZYv8bN966y1c\nunQJ8+fPR7t2T2Do0G2YObMUJ0489Cf6XZDEhx9+iO++/x6hISHo0aPHIycGfxaFhYXy96KiInh5\neaFr166Prf7/NvyjS7fHhRs3bjApKYkWi4WiKHL06NH3JbAdOnSIfn5+sgDR119/TZ1Oxy5durBd\nu3YURZEzZ87k8uXLGRgYyBUrVjy0PbvdzoCIACpeVkjmo02g0d3Iixcvcvbs2ezQoYNkTrpayXzU\nz2liau40LX3uNBv1B3ERxBeQhIGskNhh3UC0ch73lkJfEQoaPAxU+aqIBGdobhTuJfpddIbTVoC4\n7GSG/Y1pJiREYv7cvXs3Z8yYwRUrVnD9+vUURZE6nY4Gg4HR0dH3aQNUxscff8xBvXtzwtixvHr1\nKnfs2EHRXaRynJJCH4EeAR68cuXKQ89/8cUXqdPpaDabaTAYuHPnzgc+46ysLCoU0j3oATYFuBig\nBeAAgNMAugqCnFj5KOzbt48rVqzgrl27GBwczNjYWLZq1Yru7u6cP18ytW3atIne3t4cM2YMp06d\nyuQmTdhEo+G3ALUA3QDOg6QFbhYEdu3alePGjWNmZiY1GhUjI2uwsLCQJLlkyRKGh4fTx8eH4eHh\nbNasGY1GI7s4NcWPAAyqZLY74TSJ9QHorVYzJiZGps6Ijo6mWq1m3bp12a1bN5lNVqVSycJGLi4u\ntFqt1Oul7GoXFxeq1WrabLYqNBwJCQkMDAykIAgcP348ExISqNPpaDQaKQgC+/Tpw5o1azIsLEzO\nGlcqlRw/frxsmvLz86Orqyvj4+MZGxvLZ555hi1atKDFYuHQoUMpiiJJ8vbt2+zduwu9vV1Yu3bA\nQ001vy9fmkVDZCQxZQoN9euzY8+ej12+9C5WrFjB6tWrP/T4w+4BjzkQqHJk04YHbP8n8bc8+H8C\nDoeD165dk5k/H4Rp06bRw8ODjRs3pslkqkJpkZWVxYiICD755JMPpD+ojNLSUiYkJFD0EAkVaPWz\n8ssvvyRJrlmzhunp6dS4aIjvKw0S7Z2DxC+V9k1wDhQKp38hQdrWBemoEBVEptMvMQOSLOoAUOmi\nJJ4DscTpjzBBEjeaBSLAWdZJF64UlXIne/cTExPDBQsWUK/XU6lUUhTFKlnccNrs/4je711E1I8g\n1t+7L3WmmpOnTn5g2WPHjlEUxSrtmUym+2inFyxYUKWcwckhlQ0ws1LnuhVgPefA9zBMmzSJ1USR\nPQwG+osiJz/3HLOzs5mQ0IAeHoGsWTNBJnucPHmykzVVR1dB4CyAnkol+zsHB1eAwRoNLSYTPT09\nqdVqaTKpuWoV2L69RH1x48YNTp8+nWFhYQwMDJRzJ7KysqhVKukAWALQCHAWwLMAEwAaBIFqlYoG\ng6GKWFBGRgb1ej3r1q1Lk8nEp556ir169ZKIAdVqVq9ene7u7tTrtPT19eW4cePkXA21Wk29Xk9P\nT096eHhQo9HQaDTSYrGwZcuWfPbZZykIgvweDB8+nJMmTWJSUhJtNhtFUWRkZCQDAgLYqVMnmdI8\nICCgCmHhXf9Hx44daTAYHvg7PKh/keRLQ6nv1InC4MEU3dweIF9qJfLzJUao4mKK1ao9QL50LE0e\nHrT6+nL2K6888n14FJo1a8bnn3/+occf1kfiMfskXnF+foUUn/ImJCt0oXPf//BvQKFQwM3NDUaj\n8aFlJk+ejD179uD5559Hw4YN0bFjR/lYy5YtUa1aNaxatQodOnR4ZFurV6+WQv0u38ai+Yvga/GF\nzWbDsWPHMG3aNKSmpmLOi3OgbKWUfA39IJmcvCCZju7ia0i2Cxsk6sV5gIIKOPIdEBoIUrSTKyTm\nrSgAbwAOlUMKYFZCimr6FVIk1HpAeV0pRVq1AlAL8Pb2htlslk1noihi5syZGDFmBEoqSuAQHChW\nFKO8vLzK/ZWVleHatWu/88Tv4datW5LZzImKoArcKLjxwLInT56sEg56t70bN6qW37Rpk2wmg/N2\nVZBCWH0rlfMBUFhUhIfh7NmzmD9nDg4WF2NlURH+VVyMRfPno6CgCN9/b0du7lqcODEZ3bo9gz17\n9mDatGm4evUqDh48iGeysrCzQQNchxoKAJGQUlrg5YVhI0YgMzMTycnNULOmEpGRwDc7S7Bo+nT4\neHjg8x07cP78eZhMJtl8YzabUUZiO4A9kGzS6wDEAziiVqNNaipGjR4Nd3d3fPvtt7Db7fj/2jvz\nuCir749/Zt8YBgTZEZRFVNw3NE0wt0hc0nLBFLV+qamZWmqL2Vcr/Wr6LZdyKdNS075piaS55JIo\n+rVccKvcSjBBxQABUZjz++M+M8wwMzCDLAPdt6/nNTP3uXOfc5/Be557z7nnFBUV4fTp06hfvz7S\n09PRp08fNGrUCCEhIYiLi0N7AE9dvgxxVhZatGwFf39/o42oZcuWRvuAp6cnfHx8IJVK8cILL2DQ\noEFITU1FWloadDodnnrqKahUKiQmJho9qfLy8iAWi+Hn54eQkBAcO3YM58+fx8iRI0FEKCoqMv7t\n6PV65OXlITk5GRMnTrT5e5Tmo2XLkNG1Kwr++188WL4c+cuX46VZs4znc3JyIKtXD9AJvnEqFWS+\nvsjOzjbWmbdgAVYnJyP32DHc3b0b76xahS83Op6J4Y8//sChQ4cwatQoh79bEcqySRwQXj9ASQIi\ngM0ifq4qgTiM8PBwhIeHIzU1FYsWLUKXLl0gkUiwYMECxMXF2dVGZmYmWrZsCbFYjAkTJiAjIwOd\nOnWCl5cXXnrpJSQkJEAkEiFpZxJ2b90N9ARwFMBCAAPARprTYHvtD4PtkdgOoD9AhYQH5x8wg/ca\nsEVwg1ttPthWTBWYS+1gMOP1dACxgLK7EvnL8lmAlgAg640sLHp/ETJuZuD+/ft49tlnmdumVs+u\nHQQWJOY/gKRAguJitgFKLpfj8ccft/ueDo4bjJXTVqJgZQGQAag+VGHg59YVbUREBIqKiszKlEql\nxQa+wMBASKVSY91iAEMBdAEwD8wnwA9M/w4oY/345s2bCJbL4SW4zvoACJTLsWnTNuTnbxRaAgoK\npmLTpm/QtWtXuLq6IjIyEu8tXIjXX38Tx87+jq3FtxFSAPwsEqNheDhSUlJw5MgRFBcXQyoF+vcA\nYu6LcEitQIeWLfHrpUtQqVS4cOECzp07B19fXxz88UfotFq87ecHkUiEcIUCt06dwkwAy7y9ERnJ\n8gIOHz4cixYtwsKFCyESidCgQQOoVCo8ePDAzAW4oKAAfiIRPgTws0SCk6mpkMvliImJgUqlwtmz\nZ+Hp6Yns7GxcvnwZWq0WCQkJ0Ol0yMjIQF5eHr777js8++yzyMnJgZeXFzw8PLB27VrUq1cPSqUS\nRUVFOHz4MAoKCiCXy9G5c2ccPnwY7u7uiI6OxubNmxEREYErV9jz7cyZM/Hyyy+X+zdjIDMrCw+b\nmISwi4gwe2AICQmBu0yG/PnzoX/uOYgSEyG9cQMtW7Y01vlm507kz50Lg5U8f8YMbN21CyOGD7db\nDoA5eXTt2tXoUOEMXAAQYvK5kVBWk1R4mlbbKC4uFsIzaEitVtOUKVNsZtoqzdGjR8nX15fOnDlD\nhYWFNGnSJOrbt69FvbNnz5LGU0OiuSLCRyCVt4rq+dQjcT0xiZQilsHO9F89kCxMxrLbGWwYMmHJ\nabGwHOUCwmdggQE7oyQc+L/Astq5CK/1QXgH1LFXRzOZFi1aRJhgcs0cEKSgqKgokkgk5OrqapGM\nqDwePHhA418ZT/UC65FfYz9a90XZ31+yZInRJqHVao1LdabcuHGDvL29SaPRkEajITc3N3riscfI\nXyymJwBqIbiiukgkNjOhEbH1bm9XV0oUAgJuA8hHp6Pw8HYEJBlWrUgsnkbTp8+0+P7atWvJ1dWd\npk8HDenL7AauWi15eHjQxIkTaerUqeTv708SiYQUYrEx091bb71F9T09qS9ALeRy8pXJ6CmZjBrW\nr29su7i4mAY89RS5SiSk1WrpzTffNIbTkEgkJJNKqV2rVtSwYUNydXWlDh06kEwmoyeeeIKio6NJ\nKZHQlwCdA0gll5NSqaRGjRqRQqEgtVpNcrmcunfvTvXr16ewsDBSKFgo8N69exttGjExMRQXF0cu\nLi7GBEyRkZG0adMm8vT0NPZn9uzZFBoaSs2aNaMZM2ZQdnY2FRcX08cff0wJCQn07rvvlut2bW18\n2bFjB6mDgwmpqYTMTFLFxdGLL79sVufq1asU1aMHuXp7U8suXSwi3UbHxRFWrjQGKZfMmkX/N2lS\nmbJYIywsjNauXetwHwzlVTEg9wHzcTkoHH8A6F0VF3IAh29sbUev11fICLZ+/XqjL3vv3r1tJic6\ne/YsjZkwhoY/P5yi+0STZJKEGbvjweI8GYzbZwWloBGM1TEg5ILFfYoFiwv1hLCOHwG2LyJQMGqH\nCa+TQXhLsFNsZ0bw0kpi8+bNpO6gZpnzCITvQR4NPIz3wlEyMjLo0qVL5aYrLc3Nmzfpl19+MUto\nX5qsrCxat24dffbZZ3Tz5k0qKCigjpGR1F+loncAClarael/LJMzlSY5OZkCPDxIIZFQg/r1KSUl\nhb799ltSqbwJWEBi8XTS6XyM+zZMKSoqoujoJ0mlElNcnIi0AHnK5RQXF2dci09ISCAXtZoUEonR\n/jBnzhyKiIigIIWC7gBUDNAkmYyGxsVZXEOv19PAgQMpODiYOnXqRFqtlurXr0+tZDJ6T7DDtBOL\nyU0mow8BGi0Wk4dMZozLpJDLycPDg3r06EEBAQHUunVrGjFiBLVo0YLUajWlpKTQnTt3aPDgweTr\n60uBgYHUpEkTat68OXXu3JlCQ0PJ39+fnn32WeratSsFBATQ3bt3qWvXrtSrVy9jDKegoCDasGGD\nQ7+zKbbGl6UrVpCbry+pdDoaPnZsBdOXepLspZdIMXo01fP3dzh9aXJyMmk0GqPjgS1s9QEVUBL2\negUrwTzmCWzVutDRC1UyQn859mIaEbQ8mj3WDOffPw98CeAvAA3B4js1A5tD/kd4XQ62RPU3mGts\nTwCTAbiC7Z3wAHAXwGtgPpTbwRY4E8CWoQ6ArcscBJa+v9RsjfjChQt4esTT+PXvX0GNCKKjInz4\n/oeYNGmSQ/0mIkyePBmrVq2CVCqFt7c3Dh48iMDAQIfacZT8/HysWbMGGX/9hW4xMejVy1aSRUt5\n8/PzoVarjTaCQ4cO4auvtkKjUWHixHE2lxmICHv27MGePXvwyX/+g6FEuNi+PXoI7tQnTpzAqZMn\ncevWLbRp2xaPPfYYrl+/jh9++AHPDhiAz7/4AjK5HGqVCp9/8QViY2MtrlFcXIwtW7bg2rVraNu2\nLWJiYjDoySdxLiUFHhIJ0kQitC0sROL9+xgvleLnZs0QO2AAiAiJiYnIzc2FVqvF1atXMXnyZIjF\nYuj1eqxcuRJ79uxB8+bNLa5peu1FixZh//798Pf3x9y5c+Hn54erV6+ie/fuePjwIe7du4enn34a\nn376qZmLrCOIRCJU1fhy6dIlbNu2DVKpFMOGDTPGnrKXcePGoaCgAOvWrSuznq0+CPfEoRtjb+XO\nYEOFFCWaaL0jF6pkuJKoQoaOGYrN4s0sGOB5AJ+C2SmeBFtoDwEL79EUbKn8VQDHwJRGGFiMp5tg\nFtQWYEHj/wJbqDwOFtuJwBREGIAiICQ1BKkpqVCpVNi1axeefvppFIgKWEiQAQAUgMsbLrjwywV4\nenqisLAQOp1pAA3rfP311xg9ejTyBKOxRCJBp06d8NNPP5IfnxMAACAASURBVFXGrQLA7AlnzpyB\nj48PWrRoUWntPgoRAQGYk56OqVIpfMPDAbkcqWfPYkxREUQiEdYrFBBJJAgMDMSaNWtw/PhxfPDB\nB+jWrRvy8vJw4MAB7N+/3yIEOxHhm2++walffkFIWBikUim+/PJLFBUVYdiwYejevTu6tmuHCdnZ\n+EomQ8vBgxEWFga9Xo8LFy7gwoULuHjxIpRKJaZOnWpUEosXL8aECRMwf/78Cg3uhYWFuHjxIlxd\nXR853HdVKonqojKVhD18CfbffgVYUiDDUZM4NEXj2Mft27epc8/OJJIKIcU1IJwS9j68CkJXE9vC\nUmYjwD3mxoocsLAbS03sCPEgtBfe6wW7Ra7J+edA8ADhDEjbRUuJiYmUk5NDbm5urG0V2P4KbxAO\ng7T9tdSvXz+SSqUkk8koKirKLFkMEQvlnZaWZlxWmjVrlsUeDFdX1wrdn6KiIvruu+9ozZo1Rj/5\nffv2kUajIZ1OR2q1ml588cUq8413hJ07d5KnWk0jlEoKlcvJRSajBSYuubNFIpooJK0iIgoPDzcm\nF5ozZw5FR0fT1KlTLdqdNnEitdBoaA5AoXI5uel09Mwzz1BsbCyp1Wp6+eWX6ciRIzRy8GAK9vOj\nRo0akaurqzEftU6no2HDhlFwcDBFRkbSM888Q02aNCE/Pz/y9fWlTz75pDpvk1Xqwvhiqw+owHKT\nPTuu24I9M9Zu1copl2HPD8P/Gv8P9D0Bv4P5PPYA81hqDbbTOgBs53UmmL/nNgCTwHZdq8FCcQDM\nxfUAWDiPBWB/QfUAPA82K0kFi/M7FcAggEIIN27cQGhoKAul4A7gLJhr0PcABgO5RbnY/nA7u+4D\nlsxm7Nix2Lp1KwDg22+/xXDBU0SpVCIpKQlhYWEs3IXgpioSieDj44OkpCRERkZaXbrJysrCzz//\nDDc3N7Rr1w4ikQhFRUXo2bMnTpw4ASKCXq/H5s2bMWrUKOMsBQC+/PJLDBkyxBh+xR4yMjIwb948\npKWl4amnnsLYsWMrvFRioE+fPjh04gQOHDiAWDc3fDR3LtqbbCf2JcJ1Iaw2wCK+GrzGAOvLk5mZ\nmVizejWuFhbCHcDXIhH6DxhgfHLPz8/Hj8uX479ffIGjp04hKysLHTt2xLBhwxAQEIDDhw/j4MGD\nCAkJQcOGDXHo0CHs378fOp0OCQkJuHr1KjZt2oQXX3zxkfrOqVzsURJnwVacb1SxLJwaJvlQMh5e\neMj+KpoA4pfEGPHXCCT/LxmXx11mA30QgBfAArz/CLYXwpA6ahnYXonjYHGgCsBCjJ8W6hcAuAS2\n3NQAwCYwJfQuIMuT4bO7nyEzO5OFNO8EpiAAFirkLgQ/UrCgRD8CD/If4MiRIwCA9PR0xMfHG+MB\nFRQUIDY2Funp6fjvf/+LgwcPQiKR4MGDB/jzzz8RHx+PBw8e4NNPP8WwYcOM9+DkyZPo3r270b++\nR48eWL9+PebOnYuUlBQz187Ro0eb+cEbuHLlit1K4u7du2jVqhVu376NoqIi7NmzB5cuXcL8+fPt\n+n5ZNGnSBE0Et82baWmYNmcOVuXnIxfAe2o1Pn7uOWPd6G7dsH79ejzRowfy7t1DSkoKXnvtNbP2\ncnNzoZNK4VbITJLWNll11+uhyM7GkgUL0K5zZzRt2tSYN6Jbt244evQoTpw4gaioKLRv3x6nTp1C\nXFwc5HI57t27Z8yRwaldHAAzTe4G33FdJ0hKSqIhQ4ZQfHw8JScnG8sbNG1ASCrZCa3uqabVq1dT\nQUEBtYlqw7yackrOi3xELCSH6T8FCFMFryYpCM1A2ADCJGF56SfBA8rQzv/Yd3bt2kVSdynzhBoH\ngh8Ifwl1dgpLX4awHoUwZqhr164dERH98MMPpNPpzJaVNBoNXbp0ifR6PR09epR69+5tsbNbqVRS\nXl4eZWdnU3Z2NkVERFjs6q5Xr57VqK5isZgCAwMt6pfOWFdUVESpqal0/vx5ixzfa9euJY1GY9aG\nXC6vlCUrvV5PaWlplJ6eTsXFxfT+v/5FTQMCqFWjRvRFKffhzs2a0esADZbLaZRUShMAmjJhglmd\nhw8fUmTDhvSORELXAXpOJCKtVkuDBw+m2NhY0kildAqgjwEaM2QI7dmzhwICAowusxMnTiSFQkGN\nGjUid3d3owts165dqVu3bqTT6ejo0aOP3O9HpS6ML7b6gCpaEYoWjm7CYXhfk1TzLa87fPvtt+Tn\n50dTpkyhRq0bkdRDSnPnziUitr6uqqciVV8VaTpqqF23dsbYSMuWLWMuq0UmCiEMBH8Q8oTPqSDI\nBRtGrGBv0IDQD4TGgm2hEIQXQPACoQ9Y7KcXQCIXEbNZNBfamg8WziMULM5TAxMlkc/KtFotnT59\nmoiILly4QCqVymywVSgURtfV8ePHWx3olUoldezYkSQSCYnFluFBICiD0mUQbBsffPCBWVmHDh3M\nBvj09HRyd3cnACQSiSgyMtLMT3/VqlUW4T+kUqmFMinNH3/8QcuWLaOVK1fS7du3Lc7n5+dTTEyM\nMXRHz549y3TZ7NC4MR0wsVn8GzCzWRj4888/qU+XLuSj01GHJk3IRaGgCIWCOslk1BigsQA1Uqtp\n27ZtbH/FgAEUFBRkDFO+evVqKioqouvXr1Nubi5dvHiRXnvtNZo6dSqdOnWqzD5XF3VhfLHVB1Sh\n2cAHQBxYWhqvqrqIA1TzLa879OzZk2bPnk0qHxXhKxC2giQ+Etq8ZTO9v+h9kuvkpAxTksZTYzbL\nWLhwIYkaidjmuWMgvMue5iM7RrIB/BlBKShBkIAZvtVgMZr0gnJoIgz2vcH2UGwF4QqMG/TgJ7we\nFsp+A0l0ElKr1UyJjAJhE0jRQ0Htu7W3CM43e/ZsUqvV5OrqSiqVymzDkVartTrQl3fYUhC2jtKB\nAIODgy3qPP/888bz6enpRsMuAFKpVDRixIgyf8MzZ86QVqslpZDr2svLi9LT083qvPLKK6RUKo3X\nVKlUNGPGDJttrli6lJqo1fQ9QBsAqq9SUXJyMm3atIlmTJ9OK1eutNhj8uGHH9JohcKoWK4CpBKJ\n6JPly411iouLadu2bbRs2TI6fvx4mf1yFurC+GKrD6giJfEs2Aa69cJxDSyYdE1Szbe87tCjRw9q\nH9OesNpkRvANqHmn5qT2VxPShLKtoPoN6hufio8ePUpKbyVTBq1BaAZq+3hbKi4uphfGvUB+IX7U\ntENT+uGHHyh2UCyJQkSEiSA0BWEaa1M5TklhEWFMiXiAeUYRCEuEWcZGEJYJCmYqCO1BjVo0ohs3\nbtCnn35KcYPiqMfAHvSv9/5ldffyn3/+SdOmTaOEhATav3+/sfz+/fsOD/ZyuZzkcjkFBQU5rCTW\nrFljvLa1mUl4eLjx/KVLl2jUqFHk7+9PDRs2pOnTp1NhYWGZv+ETTzxh1q5UKqVx48aZ1YmKirK4\nbteuXW22qdfraeWKFRTTujX16dyZ9u7dS1PGjaNWGg3NAyharaYBvXqZzXCsKQkPG0HzahN1YXyx\n1QdUkZI4A/PZQ32hrCap5lted/jqq69I5alig7Hh3wZQeMtwchnuYmZfkCqlZjuN13y2hpSuShJL\nxdQuup3VMNuXLl0ilZeKkC20cldQCPtBaj81HT9+nPLy8mjYmGGkaaYh6QtSNnvYa3LlfwkzjjGg\ngKYBdvXrt99+I51OR3K5nGTCLt9z585RcXExbd++naRSqUOzh9mzZ9OdO3fo6aefdkhJqFQqWrBg\nAX3yySd0/vx5i8i1AKh3795GmUvPcORyOcXHx5e5NBQZGWnRZumsdwkJCSSRSIznRSIRjRw50q57\nScR2qOsUCrorKIBCgEI1Gjp27JixTlpaGvnodPSeWExbAWqnVtObr71m9zWclbowvtjqA6pISaTC\nfPOFWCh7FBaC7dk9DbZly3RX1CwwB8yLYHFCrVHNt7xuMW/ePBJrxYSPQPiExWpasmQJqRuoCZnC\nQL0L5O7rbmFA1ev1ZT7pHj9+nFxbu5obs4OYwvlk9Sdm7SQlJdEHH3xAsvoywj6UhP3oBxYHyhPk\n4u1CX3/9NRUXF1NaWprVsCIPHz6kmJgYs6drkUhEffr0oVatWpktuwDCTMUVbGnMxkB/4sQJ2rlz\np+V3yzikUikFBweTi4sLqdVqUqlUNHr0aDO55HK5UbmOGzfOajsKhYLGjh1r8x7PmjXLzI6hVCrp\nrbfeMsutkZqaajGLMRj57eHy5csUoFaTHiV2ii6urrRv3z6zer/99huNHDyY+nbtSkuXLHGKPSKP\nSl0YX2z1AVWkJBaCeTYlABgNYBdYLvZHoSdKPOjmCwfAnCxPgQWkDgZzmLTmaVfNt7zuceTIERo0\nchANGDHA+B9/1pxZpPRUkq6DjrRe1gPalUdubi55BHqw4H7ZINEqEXkEepQZ+2j5x8tJHiQnvAaW\nzGg4WJDATiBI2aBZP7g+KT2VJHeVU/zz8VRcXEx6vZ7Wr19P9YPrs+Wt1jB6PQEgT09Pyyd5FZhy\nPAHC02B2ExPFolKpjIED4+Pjy1UMSqWS1Go1RUdH02OPPUZyudzsvJeXF+3YsYOGDh1KQ4cOpV27\ndhlnCSNGjLDZro+Pj8379eDBAxo7dizJ5XISi8UklUpJIpGQRCKh2NhYunPnDn311Vfk4uJiocTK\nymNiSlFREUUEBNAssZiuALRMJKJADw/6+++/HfhrqJ048/hy/fp16tu3rzHH+MSJE60G/LTVB1SR\nkgCAQWAZBxYDKDuJgeMMBNvVDbBZxAyTc7sARFn5TlX/Fv9Yrly5QsnJyRY7mR3hzJkzFN4mnGRq\nGUW0i6Bz586V+50vNnzBZjfJMC6BQSUoDRWYt1QRCLkgdRc1LVuxjGbNmkVSuZQZwouF7y0AQcNc\nUb29vS0H4O4mM5xCpoQgKJTCwkK6desWHT9+nDIyMmj8+PE2bRlisZjEYjH5+PhQp06dLDyUD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RNBqN1QyGtvoAriQ4HKKbN2/SkiVLqHv37hYKwdoO6ZCQEFq8eHG54caDgoKsXs/Ly8umgjCE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\nh1PV/D/SOgmM65cYYgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1075c4dd0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To finish, let us look at principal component transformations. We will take the principal components from the estimator by accessing the components attribute. Each of its components is a matrix that is used to transform a vector from the original space to the transformed space. In the scatter we previously plotted, we only took into account the first two components."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_pca_components(images, n_col, n_row):\n",
      "    plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
      "    for i, comp in enumerate(images):\n",
      "        plt.subplot(n_row, n_col, i + 1)\n",
      "        plt.imshow(comp.reshape((8, 8)), interpolation='nearest')\n",
      "        plt.text(0, -1, str(i + 1) + '-component')\n",
      "        plt.xticks(())\n",
      "        plt.yticks(())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_pca_components(estimator.components_[:n_components], n_col, n_row)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x107fb6a90>"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}